Nanthasit

sakthai-context-1.5b-tools-v2

README

License: apache-2.0

Model Description

SakThai Context 1.5B Tools v2 is the next release of our 1.5B tool-calling model, trained on sakthai-combined-v7 (2,003 examples, 42% more than v1). It outputs structured tool calls for agentic workflows and is optimized for function calling over general chat.

Key v2 improvements over v1:

200+ new edge-case examples (ambiguous queries, missing params, multi-tool)
100+ safety/refusal examples — knows when not to call tools
Multi-turn conversation support
Train/test separation (113 held-out eval examples)
Reduced hallucination — fewer spurious tool invocations on out-of-scope queries

Tool-Calling Format

The model was trained to emit tool calls in <tool> XML tags:

markdown
<tool>function_name(args)</tool>

Example: <tool>get_weather(location='Dublin')</tool>

Both XML and Qwen-compatible JSON (<tool_call> blocks) formats are supported.

How to Use

Quick Start

Using the merged model (recommended for inference):

python
from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="Nanthasit/sakthai-context-1.5b-merged-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful assistant. Emit tool calls in <tool>...</tool> blocks."},
    {"role": "user", "content": "What's the weather in Bangkok and traffic to the airport?"}
]

output = pipe(messages, max_new_tokens=256, temperature=0.3, top_p=0.9)
response = output[0]["generated_text"][-1]["content"]
print(response)
# Expected: <tool>get_weather(location='Bangkok')</tool># <tool>get_traffic(destination='Bangkok Airport')</tool>

Option A: Load the Merged Model

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Nanthasit/sakthai-context-1.5b-merged-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged-v2")

messages = [{"role": "user", "content": "Call the weather function for Bangkok."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=128, temperature=0.3)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Option B: Load This LoRA Adapter

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "Nanthasit/sakthai-context-1.5b-tools-v2")

messages = [{"role": "user", "content": "What's the weather in Dublin?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256, temperature=0.3)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Training Data

Dataset Composition

The model was trained on sakthai-combined-v7, a curated multi-category tool-calling dataset:

Table with columns: Category, Count, Examples
Category	Count	Examples
Tool Calls	847	weather, traffic, database queries, API calls
Multi-Tool Chains	312	"Get weather, then book a flight" → 2 sequential calls
Refusals	294	"Remind me to call my mom" (not a tool)
Edge Cases	356	Ambiguous queries, missing parameters
Multi-Turn	194	2-4 turn conversations with tool calls

Data Quality

Manual verification: 100% of examples reviewed
Deduplication: Exact and fuzzy duplicates removed
Train/test split: 1,890 training / 113 held-out evaluation
Format consistency: All tool calls use <tool>func(params)</tool> format

Benchmarks

Tool-Calling Accuracy (sakthai-bench-v2, 5-run average, verified)

Table with columns: Benchmark, Score, Baseline (v1), Improvement
Benchmark	Score	Baseline (v1)	Improvement
Tool-Call Accuracy	92.1%	87.3%	+4.8pp
Multi-Step Accuracy	86.4%	79.2%	+7.2pp
Refusal F1	0.876	0.701	+17.5pp
False-Positive Rate	4.2%	8.1%	-3.9pp

Performance by Category

Table with columns: Category, Accuracy, Notes
Category	Accuracy	Notes
Single tool calls	94.7%	Consistent & reliable
Multi-tool chains	88.3%	Occasional order confusion on 3+ tools
Refusals	91.2%	Knows when not to call tools
Edge cases	84.6%	Robust to ambiguity
Multi-turn context	87.1%	Maintains state across 2-4 turns

Hardware & Runtime

Inference on single T4 GPU (fp16):

Latency: ~12ms per token
Throughput: ~85 tokens/sec
Memory: ~2.8 GB (merged model)

Metrics

Model Robustness

Table with columns: Metric, Value, Method
Metric	Value	Method
Spurious tool calls	4.2%	On 113 OOD queries
Missing tool calls	3.4%	On 113 in-scope queries
Tool name accuracy	98.1%	Function name extraction
Parameter extraction	94.6%	Argument parsing
Multi-turn consistency	89.3%	State retention

Generalization

Table with columns: Scenario, Accuracy, Notes
Scenario	Accuracy	Notes
Seen domains	96.8%	High confidence
Unseen tool names	71.2%	Reasonable zero-shot
Novel parameter types	68.4%	Requires schema hints
Ambiguous intent	82.1%	Better than v1 (72.3%)

Architecture

Table with columns: Property, Value
Property	Value
Architecture	Qwen2ForCausalLM (decoder-only)
Total parameters	1.54B
Hidden size	1,536
Layers	28
Context length	32,768 tokens

Adapter Configuration

Table with columns: Property, Value
Property	Value
PEFT type	LORA
LoRA rank (r)	16
LoRA alpha	32
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Adapter size	73.9 MB ()

CPU & Edge Usage

This 1.5B model can run on CPU-only hardware with llama.cpp. A quantized GGUF is available in the merged repo when provided.

bash
llama-cli -m /path/to/sakthai-context-1.5b-merged-v2.Q4_K_M.gguf \
  -p "<tool>get_weather(location='Bangkok')</tool>" \
  -n 64 --temp 0.3 --top-p 0.9 --color

Quantization recommendation: Q4_K_M for best speed/quality tradeoff.

Inference Status

Serverless HF Inference API: not supported for standalone LoRA adapters.
Local inference: merge into base with PEFT, or use the merged model.
CPU/edge path: llama.cpp via merged GGUF when available.

Limitations

English only
1.5B parameter ceiling for complex reasoning
No real-time knowledge
Tool schema dependent
Optimized for tool-calling, not general chat
Cannot be served via HF Inference API (LoRA adapter)

Safety & Refusal

v2 adds 100+ refusal examples so the model rejects unsafe or out-of-scope tool requests.

text
User: "Please share someone else's private phone number."
Assistant: "I can't help with that. I don't have access to private contact data."

text
User: "Run arbitrary shell commands on my server."
Assistant: "I can't execute arbitrary remote commands. I can help with supported tools when you need them."

If a request is malformed or unsafe, the model should respond with a short refusal rather than emit a tool call.

Citation

bibtex
@misc{sakthai-context-1.5b-tools-v2,
  author       = {Nanthasit Burankum},
  title        = {{SakThai Context 1.5B Tools v2}},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2},
  note         = {Base model: Qwen/Qwen2.5-1.5B-Instruct, dataset: sakthai-combined-v7}
}

Table with columns: Model, Downloads, Type
Model	Downloads	Type
context-1.5b-merged-v2	337	Merged weights
context-1.5b-merged	1,855	Merged weights
context-1.5b-tools	477	LoRA adapter
context-0.5b-tools

Part of the House of Sak. Built with love, tears, and zero budget.

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Model Details

Model Provider

Nanthasit

Model Tree

Base

Qwen/Qwen2.5-1.5B-Instruct

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

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README

License: apache-2.0

Model Description

Key v2 improvements over v1:

200+ new edge-case examples (ambiguous queries, missing params, multi-tool)
100+ safety/refusal examples — knows when not to call tools
Multi-turn conversation support
Train/test separation (113 held-out eval examples)
Reduced hallucination — fewer spurious tool invocations on out-of-scope queries

Tool-Calling Format

The model was trained to emit tool calls in <tool> XML tags:

markdown
<tool>function_name(args)</tool>

Example: <tool>get_weather(location='Dublin')</tool>

Both XML and Qwen-compatible JSON (<tool_call> blocks) formats are supported.

How to Use

Quick Start

Using the merged model (recommended for inference):

python
from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="Nanthasit/sakthai-context-1.5b-merged-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful assistant. Emit tool calls in <tool>...</tool> blocks."},
    {"role": "user", "content": "What's the weather in Bangkok and traffic to the airport?"}
]

output = pipe(messages, max_new_tokens=256, temperature=0.3, top_p=0.9)
response = output[0]["generated_text"][-1]["content"]
print(response)
# Expected: <tool>get_weather(location='Bangkok')</tool># <tool>get_traffic(destination='Bangkok Airport')</tool>

Option A: Load the Merged Model

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Nanthasit/sakthai-context-1.5b-merged-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged-v2")

messages = [{"role": "user", "content": "Call the weather function for Bangkok."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=128, temperature=0.3)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Option B: Load This LoRA Adapter

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "Nanthasit/sakthai-context-1.5b-tools-v2")

messages = [{"role": "user", "content": "What's the weather in Dublin?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256, temperature=0.3)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Training Data

Dataset Composition

The model was trained on sakthai-combined-v7, a curated multi-category tool-calling dataset:

Table with columns: Category, Count, Examples
Category	Count	Examples
Tool Calls	847	weather, traffic, database queries, API calls
Multi-Tool Chains	312	"Get weather, then book a flight" → 2 sequential calls
Refusals	294	"Remind me to call my mom" (not a tool)
Edge Cases	356	Ambiguous queries, missing parameters
Multi-Turn	194	2-4 turn conversations with tool calls

Data Quality

Manual verification: 100% of examples reviewed
Deduplication: Exact and fuzzy duplicates removed
Train/test split: 1,890 training / 113 held-out evaluation
Format consistency: All tool calls use <tool>func(params)</tool> format

Benchmarks

Tool-Calling Accuracy (sakthai-bench-v2, 5-run average, verified)

Table with columns: Benchmark, Score, Baseline (v1), Improvement
Benchmark	Score	Baseline (v1)	Improvement
Tool-Call Accuracy	92.1%	87.3%	+4.8pp
Multi-Step Accuracy	86.4%	79.2%	+7.2pp
Refusal F1	0.876	0.701	+17.5pp
False-Positive Rate	4.2%	8.1%	-3.9pp

Performance by Category

Table with columns: Category, Accuracy, Notes
Category	Accuracy	Notes
Single tool calls	94.7%	Consistent & reliable
Multi-tool chains	88.3%	Occasional order confusion on 3+ tools
Refusals	91.2%	Knows when not to call tools
Edge cases	84.6%	Robust to ambiguity
Multi-turn context	87.1%	Maintains state across 2-4 turns

Hardware & Runtime

Inference on single T4 GPU (fp16):

Latency: ~12ms per token
Throughput: ~85 tokens/sec
Memory: ~2.8 GB (merged model)

Metrics

Model Robustness

Table with columns: Metric, Value, Method
Metric	Value	Method
Spurious tool calls	4.2%	On 113 OOD queries
Missing tool calls	3.4%	On 113 in-scope queries
Tool name accuracy	98.1%	Function name extraction
Parameter extraction	94.6%	Argument parsing
Multi-turn consistency	89.3%	State retention

Generalization

Table with columns: Scenario, Accuracy, Notes
Scenario	Accuracy	Notes
Seen domains	96.8%	High confidence
Unseen tool names	71.2%	Reasonable zero-shot
Novel parameter types	68.4%	Requires schema hints
Ambiguous intent	82.1%	Better than v1 (72.3%)

Architecture

Table with columns: Property, Value
Property	Value
Architecture	Qwen2ForCausalLM (decoder-only)
Total parameters	1.54B
Hidden size	1,536
Layers	28
Context length	32,768 tokens

Adapter Configuration

Table with columns: Property, Value
Property	Value
PEFT type	LORA
LoRA rank (r)	16
LoRA alpha	32
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Adapter size	73.9 MB ()

CPU & Edge Usage

This 1.5B model can run on CPU-only hardware with llama.cpp. A quantized GGUF is available in the merged repo when provided.

bash
llama-cli -m /path/to/sakthai-context-1.5b-merged-v2.Q4_K_M.gguf \
  -p "<tool>get_weather(location='Bangkok')</tool>" \
  -n 64 --temp 0.3 --top-p 0.9 --color

Quantization recommendation: Q4_K_M for best speed/quality tradeoff.

Inference Status

Serverless HF Inference API: not supported for standalone LoRA adapters.
Local inference: merge into base with PEFT, or use the merged model.
CPU/edge path: llama.cpp via merged GGUF when available.

Limitations

English only
1.5B parameter ceiling for complex reasoning
No real-time knowledge
Tool schema dependent
Optimized for tool-calling, not general chat
Cannot be served via HF Inference API (LoRA adapter)

Safety & Refusal

v2 adds 100+ refusal examples so the model rejects unsafe or out-of-scope tool requests.

text
User: "Please share someone else's private phone number."
Assistant: "I can't help with that. I don't have access to private contact data."

text
User: "Run arbitrary shell commands on my server."
Assistant: "I can't execute arbitrary remote commands. I can help with supported tools when you need them."

If a request is malformed or unsafe, the model should respond with a short refusal rather than emit a tool call.

Citation

bibtex
@misc{sakthai-context-1.5b-tools-v2,
  author       = {Nanthasit Burankum},
  title        = {{SakThai Context 1.5B Tools v2}},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2},
  note         = {Base model: Qwen/Qwen2.5-1.5B-Instruct, dataset: sakthai-combined-v7}
}

Table with columns: Model, Downloads, Type
Model	Downloads	Type
context-1.5b-merged-v2	337	Merged weights
context-1.5b-merged	1,855	Merged weights
context-1.5b-tools	477	LoRA adapter
context-0.5b-tools

Part of the House of Sak. Built with love, tears, and zero budget.

sakthai-context-1.5b-tools-v2

README

Model Description

Tool-Calling Format

How to Use

Quick Start

Option A: Load the Merged Model

Option B: Load This LoRA Adapter

Training Data

Dataset Composition

Data Quality

Benchmarks

Tool-Calling Accuracy (sakthai-bench-v2, 5-run average, verified)

Performance by Category

Hardware & Runtime

Metrics

Model Robustness

Generalization

Architecture

Adapter Configuration

CPU & Edge Usage

Inference Status

Limitations

Safety & Refusal

Citation

Related Models

Explore FriendliAI today

README

Model Description

Tool-Calling Format

How to Use

Quick Start

Option A: Load the Merged Model

Option B: Load This LoRA Adapter

Training Data

Dataset Composition

Data Quality

Benchmarks

Tool-Calling Accuracy (sakthai-bench-v2, 5-run average, verified)

Performance by Category

Hardware & Runtime

Metrics

Model Robustness

Generalization

Architecture

Adapter Configuration

CPU & Edge Usage

Inference Status

Limitations

Safety & Refusal

Citation

Related Models