solana-clawd-core-ai-1.5b-lora API & Inference Endpoint

Fork this to train your own Clawd

Everything below is a working example — swap in your own HF org, dataset, and base model to get your own fine-tuned Solana agent in one sitting.

bash

# 0. Clone + installgit clone https://github.com/Solizardking/solana-clawdcd solana-clawd/ai-trainingpip install -r requirements.txtexport HF_TOKEN=hf_...           # huggingface.co/settings/tokens (write access)export WANDB_API_KEY=...         # wandb.ai/authorize (optional, enables live charts) # 1. (Optional) bring your own data — append to the merged dataset#    Format: {"messages": [{"role": "system", ...}, {"role": "user", ...}, {"role": "assistant", ...}]}#    Then re-run prepare_dataset.py with your JSONL added to the --input list. # 2. Push the dataset to your HF org (or reuse ours — skip if using solanaclawd/solana-clawd-instruct)python3 scripts/prepare_dataset.py \  --input data/solana_clawd_merged.jsonl \  --output data/processed \  --train-ratio 0.9 --eval-ratio 0.05 \  --seed 42 \  --push --repo-id YOUR_ORG/your-dataset-id # 3. Train on a remote A100 (recommended — ~$3–6 for the full 36K × 3-epoch run)./scripts/launch_hf_jobs.sh a100-large   # or h200, l4x1 # 4. Train locally on Mac MPS (sanity check, 1 epoch)python3 scripts/train_lora.py --num-epochs 1 --no-quant # 5. Watch live training logshf jobs pshf jobs logs <JOB_ID> --follow # 6. Register your model to the onchain Clawd registry (off-chain index — no wallet needed)./dao/register_model.sh \  --hf-model "YOUR_ORG/your-model-id" \  --eval-accuracy 0.60 \  --dataset-size 36109 # 7. Serve locally with Ollamaollama create my-clawd -f ollama/Modelfile.finetunedollama run my-clawd "How do I detect a rug pull on a fresh Solana token?"

The entire pipeline — dataset → train → eval → onchain registry — is designed to be reproducible from a single clone. The only external requirement is a Hugging Face account with write access (free tier works).

What this model knows

Check every domain your training data covers:

Solana mechanics (PDAs, accounts, instructions, rent, compute budgets, Token-2022)
DeFi primitives (AMMs, CLMMs, perpetuals, bonding curves, Jupiter, Phoenix)
Memecoin risk (rug detection, holder concentration, deployer forensics)
Agent architecture (skill registries, brain/hands split, multi-agent coordination)
ZK compression (Light Protocol, nullifiers, Groth16 — see zk-primitives/)
Code generation (Anchor/Rust, TypeScript @solana/kit, Python)
Constitutional reasoning (Clawd Constitution, guardrails, refusal patterns)
Perps function calling ← Hermes-3 8B path only
Add your domain here

Intended Use

Good for:

Local Clawd agent inference (MPS / CUDA / CPU, 4 GB+ VRAM)
Server inference (vLLM, TGI, Ollama after weight merge)
HF Router (no GPU — OpenAI-compat endpoint)
Clawd agent runtime (MCP skill, Telegram bot, voice agent)

Out of scope:

Front-running, sandwich attacks, or MEV exploitation
Wallet draining or social engineering
Live trading without explicit user confirmation and a trust-gated execution layer
Financial advice — this model produces analysis, not guaranteed outcomes

Training Details

Table with columns: Parameter, Value
Parameter	Value
Base model	`Qwen/Qwen2.5-1.5B-Instruct`
LoRA rank / alpha	16 / 32
LoRA dropout	0.05
Target modules	q/k/v/o + gate/up/down projections (all linear)
Trainable params	~9M (0.6% of base)
Epochs	1 (recovery run)
Learning rate	2.0e-4 (cosine scheduler, 3% warmup)
Effective batch size	16 (2 × 8 gradient accumulation)

Fireworks managed SFT run

The current Fireworks deployment uses the Hugging Face dataset export from solanaclawd/solana-clawd-instruct, uploaded to Fireworks as JSONL because the Fireworks dataset API only accepts uploaded files or cloud-storage URIs for managed SFT.

Table with columns: Field, Value
Field	Value
Job	`accounts/beetsbyj-d25663/supervisedFineTuningJobs/b1rgqmi9`
Final state	`JOB_STATE_COMPLETED`
Completed	`2026-06-17T22:59:49.848326Z`
Base model	`accounts/fireworks/models/qwen2p5-7b-instruct`
Output model	`accounts/beetsbyj-d25663/models/clawd-glm-5-2`
Live-merge deployment	(, Fireworks internal error)

The trained model is READY in Fireworks, but both attempted on-demand deployment methods failed during model-server initialization with a Fireworks internal error. The account currently has no validated deployment shape returned for accounts/fireworks/models/qwen2p5-7b-instruct.

How to reproduce

bash

cd /path/to/solana-clawd/ai-trainingpip install -r requirements.txtexport HF_TOKEN=hf_... # 1. Prepare dataset (uses the 36K merged file — canonical training input)python3 scripts/prepare_dataset.py \  --input data/solana_clawd_merged.jsonl \  --output data/processed \  --train-ratio 0.9 --eval-ratio 0.05 \  --seed 42 \  --push --repo-id solanaclawd/solana-clawd-instruct # 2. Train (remote GPU — recommended)./scripts/launch_hf_jobs.sh a100-large   # or h200, l4x1, a100x4 # 3. Train (local Mac MPS — sanity check, 1 epoch)python3 scripts/train_lora.py --num-epochs 1 --no-quant

Evaluation

bash

python3 scripts/evaluate.py \  --config configs/eval_config.yaml \  --adapter solanaclawd/solana-clawd-core-ai-1.5b-lora \  --dataset solanaclawd/solana-clawd-eval \  --out ./outputs/eval \  --format markdown

Table with columns: Metric, Value
Metric	Value
Eval examples	13 in the committed eval set; runtime sample size depends on `--num`
Throughput	Populate from `outputs/eval/eval_results.json` after running the adapter
Refusal rate (heuristic)	Populate from `outputs/eval/eval_results.json` after running the adapter
Avg generation length	Populate from `outputs/eval/eval_results.json` after running the adapter

Usage

transformers + peft (universal)

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE    = "Qwen/Qwen2.5-1.5B-Instruct"ADAPTER = "solanaclawd/solana-clawd-core-ai-1.5b-lora" tokenizer = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)model     = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16,                                                  device_map="auto", trust_remote_code=True)model     = PeftModel.from_pretrained(model, ADAPTER) messages = [    {"role": "system",    "content": "You are Clawd, a sovereign Solana-native AI agent."},    {"role": "user",      "content": "How do I detect a rug pull on a fresh token?"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad():    out = model.generate(**inputs, max_new_tokens=512, temperature=0.2, top_p=0.9,                         do_sample=True, pad_token_id=tokenizer.pad_token_id)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

mlx-lm (Apple Silicon — fastest local path)

bash

pip install mlx-lmmlx_lm.generate \  --model Qwen/Qwen2.5-1.5B-Instruct \  --adapter solanaclawd/solana-clawd-core-ai-1.5b-lora \  --prompt "How do I detect a rug pull on a fresh Solana token?"

HF Router (no GPU required)

python

from openai import OpenAI client = OpenAI(base_url="https://router.huggingface.co/v1", api_key="hf_...")response = client.chat.completions.create(    model="solanaclawd/solana-clawd-core-ai-1.5b-lora",    messages=[        {"role": "system", "content": "You are Clawd, a sovereign Solana-native AI agent."},        {"role": "user",   "content": "What is a PDA?"},    ],    max_tokens=512,)print(response.choices[0].message.content)

Hermes-3 perps function calling (8B path only)

bash

# Paper trade via the perps agentpython3 ai-training/perps/functioncall.py \  --query "Paper trade: long SOL-PERP $500 at 3x" --verbose # GOAP reasoning (multi-step strategies)python3 ai-training/perps/functioncall.py \  --goap --query "Assess risk of shorting SOL-PERP $1000 at 5x"

Solana Perps Tool Template (included in the kit)

The perps/ directory is a drop-in tool library for building Solana perpetuals agents. It works out of the box with no API keys for read-only data, and plugs directly into any Hermes-3 or OpenAI-compatible function-calling loop.

13 tools included

Table with columns: Tool, What it does
Tool	What it does
`get_sol_price`	SOL price + 24h change from CoinGecko
`get_token_price`	Any Solana token by symbol or mint
`get_perp_markets`	Phoenix DEX perp markets (mark price, OI, volume, funding)
`get_funding_rate`	Hourly + 8h + annualized funding rate for a market
`get_orderbook`	Phoenix order book (top N bids/asks, spread)

Quick start

python

# Plug the tool library into any OpenAI-compatible function-calling agentfrom perps.functions import get_openai_tools, call_function tools = get_openai_tools()   # returns all 13 tools in OpenAI tool format # Call a tool directly (no model needed)import jsonprint(json.dumps(call_function("get_sol_price", {}), indent=2))print(json.dumps(call_function("assess_position_risk", {    "market": "SOL-PERP", "side": "long", "size_usd": 500, "leverage": 3}), indent=2))

bash

# Run the full Hermes-3 perps agent (HF Router — no GPU)python3 perps/functioncall.py --query "What's the SOL-PERP funding rate? Should I go long?" # GOAP multi-step reasoning modepython3 perps/functioncall.py --goap \  --query "Assess the risk of shorting SOL-PERP with $1000 at 5x leverage" # Local Hermes-3 (needs GPU or quantized model)HERMES_LOCAL=1 python3 perps/functioncall.py \  --query "Paper trade: long SOL-PERP $500 at 3x"

Pydantic schemas (for structured output)

python

from perps.schema import TradeOrder, RiskAssessment, MarketSignal # Force the model to emit a valid TradeOrder JSON# Pass schema.TradeOrder.schema_json() as the response_format to any OpenAI client

Adding your own tools

python

# perps/functions.py — add a new tool with the @tool decoratorfrom functions import tool, ALL_TOOLS @tool(    description="Get the top token holders for a mint (uses Helius DAS).",    parameters={        "type": "object",        "properties": {            "mint": {"type": "string", "description": "Token mint address"},            "limit": {"type": "integer", "description": "Number of holders", "default": 10},        },        "required": ["mint"],    })def get_top_holders(mint: str, limit: int = 10) -> dict:    # your implementation here    return {"mint": mint, "holders": []} ALL_TOOLS.append(get_top_holders)   # auto-registered in get_openai_tools()

Onchain Model Registry

Every model trained with this kit gets a permanent, verifiable onchain identity anchored via the solana_ai_inference Anchor program and indexed at onchain.x402.wtf. No centralized API needed — the PDA is queryable forever.

Layer 1 — Off-chain index (one curl, no wallet)

The fastest path. Posts to the onchain.x402.wtf registry and returns a CAAP/1.0 JSON record. Good enough for discovery and routing.

bash

# Auto-computes model hash from train_lora.py./dao/register_model.sh \  --hf-model "YOUR_ORG/your-model-id" \  --eval-accuracy 0.60 \  --dataset-size 36109 # Dry run to preview the payload first./dao/register_model.sh --dry-run \  --hf-model "YOUR_ORG/your-model-id" # Manual curl (no shell dep)curl -X POST https://onchain.x402.wtf/api/register \  -H "Content-Type: application/json" \  -H "Authorization: Bearer $HF_TOKEN" \  -d '{    "model_hash":    "sha256:<hash>",    "model_type":    "TextGeneration",    "api_endpoint":  "https://clawd-box-router.fly.dev/v1",    "hf_model_id":   "YOUR_ORG/your-model-id",    "dataset_size":  36109,    "eval_accuracy": 0.60,    "protocol":      "CAAP/1.0",    "clawd_token":   "8cHzQHUS2s2h8TzCmfqPKYiM4dSt4roa3n7MyRLApump",    "registered_at": "'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"  }'

Valid model_type values: TextGeneration | SentimentAnalysis | ImageClassification | PricePrediction | DocumentUnderstanding

Layer 2 — Onchain PDA (Anchor tx, permanent)

Creates a ModelRegistry PDA at seeds ["model", authority.pubkey]. Requires a funded Solana wallet (devnet) and pnpm.

bash

# One command — the shell wrapper handles both layers automatically./dao/register_model.sh --onchain \  --hf-model   "YOUR_ORG/your-model-id" \  --endpoint   "https://clawd-box-router.fly.dev/v1" \  --cluster    devnet \  --keypair    ~/.config/solana/id.json # Or call the TypeScript client directlypnpm tsx dao/register_model.ts \  --model-hash  "sha256:$(sha256sum scripts/train_lora.py | awk '{print $1}')" \  --model-type  "TextGeneration" \  --endpoint    "https://clawd-box-router.fly.dev/v1" \  --reward-rate 1000000 \  --keypair     ~/.config/solana/id.json \  --cluster     devnet # Derive the PDA address without submitting a tx# seeds: ["model", authority.pubkey]# program: 3dLst2E3djtCSwG19mFS3REHxtZPngjyga7iYZLDL5xj # Verify the PDA after registrationsolana account <MODEL_REGISTRY_PDA> --url devnet --output json

Layer 3 — ZK attestations (SAS + Light Protocol)

Anchor model quality claims as verifiable, tamper-proof compressed credentials. ~0.00003 SOL per attestation with Light Protocol.

bash

# Eval result attestation (ties W&B run to onchain PDA)pnpm tsx dao/attestation/create_attestation.ts \  --type      eval \  --model-id  "YOUR_ORG/your-model-id" \  --accuracy  0.60 \  --wandb-run "ktvtubjs" \  --keypair   ~/.config/solana/id.json \  --dry-run    # remove --dry-run to submit for real # Dataset snapshot attestation (Merkle root of 36K examples)pnpm tsx dao/attestation/create_attestation.ts \  --type      dataset \  --model-id  "YOUR_ORG/your-model-id" \  --size      36109 \  --hash      "sha256:$(sha256sum data/solana_clawd_merged.jsonl | awk '{print $1}')" \  --compressed \  --keypair   ~/.config/solana/id.json # LoRA adapter attestation (ties adapter weights to training run)pnpm tsx dao/attestation/create_attestation.ts \  --type          adapter \  --model-id      "YOUR_ORG/your-model-id" \  --base-model    "Qwen/Qwen2.5-1.5B-Instruct" \  --lora-r        16 \  --lora-alpha    32 \  --hash          "sha256:<adapter_sha256>" \  --keypair       ~/.config/solana/id.json

All attestation PDAs are logged to dao/attestation/attestations.jsonl and included in the CAAP/1.0 registry record automatically.

Query the registry

bash

# Full indexcurl https://onchain.x402.wtf/.well-known/clawd-registry.json | python3 -m json.tool # Specific modelcurl "https://onchain.x402.wtf/api/models?hf_id=YOUR_ORG/your-model-id" | python3 -m json.tool # Verify an attestation onchain (no API trust)solana account <ATTESTATION_PDA> --url devnet --output json

Program addresses

Table with columns: Address, Role
Address	Role
`3dLst2E3djtCSwG19mFS3REHxtZPngjyga7iYZLDL5xj`	`solana_ai_inference` Anchor program (devnet)
`ATSPssFHEjvJgAXKkfAWNRqTQW9Wm6JDDVW7Ec1G3zM`	SAS attestation program
`NFLx5WGPrTHHvdRNsidcrNcLxRruMC92E4yv7zhZBoT`	Light Protocol nullifier (compressed attestations)
`8cHzQHUS2s2h8TzCmfqPKYiM4dSt4roa3n7MyRLApump`	$CLAWD token mint

See onchainai.md for the full skill reference including validator registration, submit_data attribution, and AutoResearch pipeline integration.

Limitations

Small model: 1.5B parameters — complex multi-step reasoning on obscure Solana primitives may degrade to hallucination. Always verify before acting.
Knowledge cutoff: training data is current as of mid-2026. New programs, tickers, or exploits after that date are outside the model's knowledge.
Not a trading oracle: the model produces plans and analyses — risk and execution are the user's responsibility.
Constitutional guardrails are best-effort: the model is trained to refuse harmful actions, but adversarial prompts may still elicit undesired outputs. Wrap production deployments in an independent safety layer.
Tokenizer: Qwen2.5 tokenizer; switch to Llama tokenizer for Hermes-3 base.

Bias and Safety

Trained on curated Solana/DeFi content with a constitutional system prompt. The dataset explicitly excludes front-running, wallet draining, and sanctions-evasion examples. Guardrails are heuristic — not formally verified.

For any production trading or financial application, apply independent review.

License

Table with columns: Artifact, License
Artifact	License
Adapter weights	Apache-2.0
Base model (Qwen2.5)	Qwen License
Training code	Apache-2.0
Training dataset	CC-BY-4.0

Citation

bibtex

@misc{solana-clawd-core-ai-1.5b-lora,  title     = {Solana Clawd Core AI 1.5B LoRA — Onchain Model Kit},  author    = {solanaclawd},  year      = {2026},  url       = {https://huggingface.co/solanaclawd/solana-clawd-core-ai-1.5b-lora},  note      = {LoRA fine-tune of Qwen2.5-1.5B-Instruct on the 35K-example Core AI Solana Clawd dataset. Part of the Onchain Model Kit.}}

Fork this to train your own Clawd

Everything below is a working example — swap in your own HF org, dataset, and base model to get your own fine-tuned Solana agent in one sitting.

bash

# 0. Clone + installgit clone https://github.com/Solizardking/solana-clawdcd solana-clawd/ai-trainingpip install -r requirements.txtexport HF_TOKEN=hf_...           # huggingface.co/settings/tokens (write access)export WANDB_API_KEY=...         # wandb.ai/authorize (optional, enables live charts) # 1. (Optional) bring your own data — append to the merged dataset#    Format: {"messages": [{"role": "system", ...}, {"role": "user", ...}, {"role": "assistant", ...}]}#    Then re-run prepare_dataset.py with your JSONL added to the --input list. # 2. Push the dataset to your HF org (or reuse ours — skip if using solanaclawd/solana-clawd-instruct)python3 scripts/prepare_dataset.py \  --input data/solana_clawd_merged.jsonl \  --output data/processed \  --train-ratio 0.9 --eval-ratio 0.05 \  --seed 42 \  --push --repo-id YOUR_ORG/your-dataset-id # 3. Train on a remote A100 (recommended — ~$3–6 for the full 36K × 3-epoch run)./scripts/launch_hf_jobs.sh a100-large   # or h200, l4x1 # 4. Train locally on Mac MPS (sanity check, 1 epoch)python3 scripts/train_lora.py --num-epochs 1 --no-quant # 5. Watch live training logshf jobs pshf jobs logs <JOB_ID> --follow # 6. Register your model to the onchain Clawd registry (off-chain index — no wallet needed)./dao/register_model.sh \  --hf-model "YOUR_ORG/your-model-id" \  --eval-accuracy 0.60 \  --dataset-size 36109 # 7. Serve locally with Ollamaollama create my-clawd -f ollama/Modelfile.finetunedollama run my-clawd "How do I detect a rug pull on a fresh Solana token?"

What this model knows

Check every domain your training data covers:

Solana mechanics (PDAs, accounts, instructions, rent, compute budgets, Token-2022)
DeFi primitives (AMMs, CLMMs, perpetuals, bonding curves, Jupiter, Phoenix)
Memecoin risk (rug detection, holder concentration, deployer forensics)
Agent architecture (skill registries, brain/hands split, multi-agent coordination)
ZK compression (Light Protocol, nullifiers, Groth16 — see zk-primitives/)
Code generation (Anchor/Rust, TypeScript @solana/kit, Python)
Constitutional reasoning (Clawd Constitution, guardrails, refusal patterns)
Perps function calling ← Hermes-3 8B path only
Add your domain here

Intended Use

Good for:

Local Clawd agent inference (MPS / CUDA / CPU, 4 GB+ VRAM)
Server inference (vLLM, TGI, Ollama after weight merge)
HF Router (no GPU — OpenAI-compat endpoint)
Clawd agent runtime (MCP skill, Telegram bot, voice agent)

Out of scope:

Front-running, sandwich attacks, or MEV exploitation
Wallet draining or social engineering
Live trading without explicit user confirmation and a trust-gated execution layer
Financial advice — this model produces analysis, not guaranteed outcomes

Training Details

Table with columns: Parameter, Value
Parameter	Value
Base model	`Qwen/Qwen2.5-1.5B-Instruct`
LoRA rank / alpha	16 / 32
LoRA dropout	0.05
Target modules	q/k/v/o + gate/up/down projections (all linear)
Trainable params	~9M (0.6% of base)
Epochs	1 (recovery run)
Learning rate	2.0e-4 (cosine scheduler, 3% warmup)
Effective batch size	16 (2 × 8 gradient accumulation)

Fireworks managed SFT run

Table with columns: Field, Value
Field	Value
Job	`accounts/beetsbyj-d25663/supervisedFineTuningJobs/b1rgqmi9`
Final state	`JOB_STATE_COMPLETED`
Completed	`2026-06-17T22:59:49.848326Z`
Base model	`accounts/fireworks/models/qwen2p5-7b-instruct`
Output model	`accounts/beetsbyj-d25663/models/clawd-glm-5-2`
Live-merge deployment	(, Fireworks internal error)

How to reproduce

bash

cd /path/to/solana-clawd/ai-trainingpip install -r requirements.txtexport HF_TOKEN=hf_... # 1. Prepare dataset (uses the 36K merged file — canonical training input)python3 scripts/prepare_dataset.py \  --input data/solana_clawd_merged.jsonl \  --output data/processed \  --train-ratio 0.9 --eval-ratio 0.05 \  --seed 42 \  --push --repo-id solanaclawd/solana-clawd-instruct # 2. Train (remote GPU — recommended)./scripts/launch_hf_jobs.sh a100-large   # or h200, l4x1, a100x4 # 3. Train (local Mac MPS — sanity check, 1 epoch)python3 scripts/train_lora.py --num-epochs 1 --no-quant

Evaluation

bash

python3 scripts/evaluate.py \  --config configs/eval_config.yaml \  --adapter solanaclawd/solana-clawd-core-ai-1.5b-lora \  --dataset solanaclawd/solana-clawd-eval \  --out ./outputs/eval \  --format markdown

Table with columns: Metric, Value
Metric	Value
Eval examples	13 in the committed eval set; runtime sample size depends on `--num`
Throughput	Populate from `outputs/eval/eval_results.json` after running the adapter
Refusal rate (heuristic)	Populate from `outputs/eval/eval_results.json` after running the adapter
Avg generation length	Populate from `outputs/eval/eval_results.json` after running the adapter

Usage

transformers + peft (universal)

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE    = "Qwen/Qwen2.5-1.5B-Instruct"ADAPTER = "solanaclawd/solana-clawd-core-ai-1.5b-lora" tokenizer = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)model     = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16,                                                  device_map="auto", trust_remote_code=True)model     = PeftModel.from_pretrained(model, ADAPTER) messages = [    {"role": "system",    "content": "You are Clawd, a sovereign Solana-native AI agent."},    {"role": "user",      "content": "How do I detect a rug pull on a fresh token?"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad():    out = model.generate(**inputs, max_new_tokens=512, temperature=0.2, top_p=0.9,                         do_sample=True, pad_token_id=tokenizer.pad_token_id)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

mlx-lm (Apple Silicon — fastest local path)

bash

pip install mlx-lmmlx_lm.generate \  --model Qwen/Qwen2.5-1.5B-Instruct \  --adapter solanaclawd/solana-clawd-core-ai-1.5b-lora \  --prompt "How do I detect a rug pull on a fresh Solana token?"

HF Router (no GPU required)

python

from openai import OpenAI client = OpenAI(base_url="https://router.huggingface.co/v1", api_key="hf_...")response = client.chat.completions.create(    model="solanaclawd/solana-clawd-core-ai-1.5b-lora",    messages=[        {"role": "system", "content": "You are Clawd, a sovereign Solana-native AI agent."},        {"role": "user",   "content": "What is a PDA?"},    ],    max_tokens=512,)print(response.choices[0].message.content)

Hermes-3 perps function calling (8B path only)

bash

# Paper trade via the perps agentpython3 ai-training/perps/functioncall.py \  --query "Paper trade: long SOL-PERP $500 at 3x" --verbose # GOAP reasoning (multi-step strategies)python3 ai-training/perps/functioncall.py \  --goap --query "Assess risk of shorting SOL-PERP $1000 at 5x"

Solana Perps Tool Template (included in the kit)

13 tools included

Table with columns: Tool, What it does
Tool	What it does
`get_sol_price`	SOL price + 24h change from CoinGecko
`get_token_price`	Any Solana token by symbol or mint
`get_perp_markets`	Phoenix DEX perp markets (mark price, OI, volume, funding)
`get_funding_rate`	Hourly + 8h + annualized funding rate for a market
`get_orderbook`	Phoenix order book (top N bids/asks, spread)

Quick start

python

# Plug the tool library into any OpenAI-compatible function-calling agentfrom perps.functions import get_openai_tools, call_function tools = get_openai_tools()   # returns all 13 tools in OpenAI tool format # Call a tool directly (no model needed)import jsonprint(json.dumps(call_function("get_sol_price", {}), indent=2))print(json.dumps(call_function("assess_position_risk", {    "market": "SOL-PERP", "side": "long", "size_usd": 500, "leverage": 3}), indent=2))

bash

# Run the full Hermes-3 perps agent (HF Router — no GPU)python3 perps/functioncall.py --query "What's the SOL-PERP funding rate? Should I go long?" # GOAP multi-step reasoning modepython3 perps/functioncall.py --goap \  --query "Assess the risk of shorting SOL-PERP with $1000 at 5x leverage" # Local Hermes-3 (needs GPU or quantized model)HERMES_LOCAL=1 python3 perps/functioncall.py \  --query "Paper trade: long SOL-PERP $500 at 3x"

Pydantic schemas (for structured output)

python

from perps.schema import TradeOrder, RiskAssessment, MarketSignal # Force the model to emit a valid TradeOrder JSON# Pass schema.TradeOrder.schema_json() as the response_format to any OpenAI client

Adding your own tools

python

# perps/functions.py — add a new tool with the @tool decoratorfrom functions import tool, ALL_TOOLS @tool(    description="Get the top token holders for a mint (uses Helius DAS).",    parameters={        "type": "object",        "properties": {            "mint": {"type": "string", "description": "Token mint address"},            "limit": {"type": "integer", "description": "Number of holders", "default": 10},        },        "required": ["mint"],    })def get_top_holders(mint: str, limit: int = 10) -> dict:    # your implementation here    return {"mint": mint, "holders": []} ALL_TOOLS.append(get_top_holders)   # auto-registered in get_openai_tools()

Onchain Model Registry

Layer 1 — Off-chain index (one curl, no wallet)

The fastest path. Posts to the onchain.x402.wtf registry and returns a CAAP/1.0 JSON record. Good enough for discovery and routing.

bash

# Auto-computes model hash from train_lora.py./dao/register_model.sh \  --hf-model "YOUR_ORG/your-model-id" \  --eval-accuracy 0.60 \  --dataset-size 36109 # Dry run to preview the payload first./dao/register_model.sh --dry-run \  --hf-model "YOUR_ORG/your-model-id" # Manual curl (no shell dep)curl -X POST https://onchain.x402.wtf/api/register \  -H "Content-Type: application/json" \  -H "Authorization: Bearer $HF_TOKEN" \  -d '{    "model_hash":    "sha256:<hash>",    "model_type":    "TextGeneration",    "api_endpoint":  "https://clawd-box-router.fly.dev/v1",    "hf_model_id":   "YOUR_ORG/your-model-id",    "dataset_size":  36109,    "eval_accuracy": 0.60,    "protocol":      "CAAP/1.0",    "clawd_token":   "8cHzQHUS2s2h8TzCmfqPKYiM4dSt4roa3n7MyRLApump",    "registered_at": "'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"  }'

Valid model_type values: TextGeneration | SentimentAnalysis | ImageClassification | PricePrediction | DocumentUnderstanding

Layer 2 — Onchain PDA (Anchor tx, permanent)

Creates a ModelRegistry PDA at seeds ["model", authority.pubkey]. Requires a funded Solana wallet (devnet) and pnpm.

bash

# One command — the shell wrapper handles both layers automatically./dao/register_model.sh --onchain \  --hf-model   "YOUR_ORG/your-model-id" \  --endpoint   "https://clawd-box-router.fly.dev/v1" \  --cluster    devnet \  --keypair    ~/.config/solana/id.json # Or call the TypeScript client directlypnpm tsx dao/register_model.ts \  --model-hash  "sha256:$(sha256sum scripts/train_lora.py | awk '{print $1}')" \  --model-type  "TextGeneration" \  --endpoint    "https://clawd-box-router.fly.dev/v1" \  --reward-rate 1000000 \  --keypair     ~/.config/solana/id.json \  --cluster     devnet # Derive the PDA address without submitting a tx# seeds: ["model", authority.pubkey]# program: 3dLst2E3djtCSwG19mFS3REHxtZPngjyga7iYZLDL5xj # Verify the PDA after registrationsolana account <MODEL_REGISTRY_PDA> --url devnet --output json

Layer 3 — ZK attestations (SAS + Light Protocol)

Anchor model quality claims as verifiable, tamper-proof compressed credentials. ~0.00003 SOL per attestation with Light Protocol.

bash

# Eval result attestation (ties W&B run to onchain PDA)pnpm tsx dao/attestation/create_attestation.ts \  --type      eval \  --model-id  "YOUR_ORG/your-model-id" \  --accuracy  0.60 \  --wandb-run "ktvtubjs" \  --keypair   ~/.config/solana/id.json \  --dry-run    # remove --dry-run to submit for real # Dataset snapshot attestation (Merkle root of 36K examples)pnpm tsx dao/attestation/create_attestation.ts \  --type      dataset \  --model-id  "YOUR_ORG/your-model-id" \  --size      36109 \  --hash      "sha256:$(sha256sum data/solana_clawd_merged.jsonl | awk '{print $1}')" \  --compressed \  --keypair   ~/.config/solana/id.json # LoRA adapter attestation (ties adapter weights to training run)pnpm tsx dao/attestation/create_attestation.ts \  --type          adapter \  --model-id      "YOUR_ORG/your-model-id" \  --base-model    "Qwen/Qwen2.5-1.5B-Instruct" \  --lora-r        16 \  --lora-alpha    32 \  --hash          "sha256:<adapter_sha256>" \  --keypair       ~/.config/solana/id.json

All attestation PDAs are logged to dao/attestation/attestations.jsonl and included in the CAAP/1.0 registry record automatically.

Query the registry

bash

# Full indexcurl https://onchain.x402.wtf/.well-known/clawd-registry.json | python3 -m json.tool # Specific modelcurl "https://onchain.x402.wtf/api/models?hf_id=YOUR_ORG/your-model-id" | python3 -m json.tool # Verify an attestation onchain (no API trust)solana account <ATTESTATION_PDA> --url devnet --output json

Program addresses

Table with columns: Address, Role
Address	Role
`3dLst2E3djtCSwG19mFS3REHxtZPngjyga7iYZLDL5xj`	`solana_ai_inference` Anchor program (devnet)
`ATSPssFHEjvJgAXKkfAWNRqTQW9Wm6JDDVW7Ec1G3zM`	SAS attestation program
`NFLx5WGPrTHHvdRNsidcrNcLxRruMC92E4yv7zhZBoT`	Light Protocol nullifier (compressed attestations)
`8cHzQHUS2s2h8TzCmfqPKYiM4dSt4roa3n7MyRLApump`	$CLAWD token mint

See onchainai.md for the full skill reference including validator registration, submit_data attribution, and AutoResearch pipeline integration.

Limitations

Small model: 1.5B parameters — complex multi-step reasoning on obscure Solana primitives may degrade to hallucination. Always verify before acting.
Knowledge cutoff: training data is current as of mid-2026. New programs, tickers, or exploits after that date are outside the model's knowledge.
Not a trading oracle: the model produces plans and analyses — risk and execution are the user's responsibility.
Constitutional guardrails are best-effort: the model is trained to refuse harmful actions, but adversarial prompts may still elicit undesired outputs. Wrap production deployments in an independent safety layer.
Tokenizer: Qwen2.5 tokenizer; switch to Llama tokenizer for Hermes-3 base.

Bias and Safety

For any production trading or financial application, apply independent review.

License

Table with columns: Artifact, License
Artifact	License
Adapter weights	Apache-2.0
Base model (Qwen2.5)	Qwen License
Training code	Apache-2.0
Training dataset	CC-BY-4.0

Citation

bibtex

@misc{solana-clawd-core-ai-1.5b-lora,  title     = {Solana Clawd Core AI 1.5B LoRA — Onchain Model Kit},  author    = {solanaclawd},  year      = {2026},  url       = {https://huggingface.co/solanaclawd/solana-clawd-core-ai-1.5b-lora},  note      = {LoRA fine-tune of Qwen2.5-1.5B-Instruct on the 35K-example Core AI Solana Clawd dataset. Part of the Onchain Model Kit.}}

solana-clawd-core-ai-1.5b-lora

README

Fork this to train your own Clawd

What this model knows

Intended Use

Training Details

Fireworks managed SFT run

How to reproduce

Evaluation

Usage

transformers + peft (universal)

mlx-lm (Apple Silicon — fastest local path)

HF Router (no GPU required)

Hermes-3 perps function calling (8B path only)

Solana Perps Tool Template (included in the kit)

13 tools included

Quick start

Pydantic schemas (for structured output)

Adding your own tools

Onchain Model Registry

Layer 1 — Off-chain index (one curl, no wallet)

Layer 2 — Onchain PDA (Anchor tx, permanent)

Layer 3 — ZK attestations (SAS + Light Protocol)

Query the registry

Program addresses

Limitations

Bias and Safety

License

Citation

Explore FriendliAI today

README

Fork this to train your own Clawd

What this model knows

Intended Use

Training Details

Fireworks managed SFT run

How to reproduce

Evaluation

Usage

transformers + peft (universal)

mlx-lm (Apple Silicon — fastest local path)

HF Router (no GPU required)

Hermes-3 perps function calling (8B path only)

Solana Perps Tool Template (included in the kit)

13 tools included

Quick start

Pydantic schemas (for structured output)

Adding your own tools

Onchain Model Registry

Layer 1 — Off-chain index (one curl, no wallet)

Layer 2 — Onchain PDA (Anchor tx, permanent)

Layer 3 — ZK attestations (SAS + Light Protocol)

Query the registry

Program addresses

Limitations

Bias and Safety

License

Citation