chinapao

Fawen-1.0-35B

README

License: apache-2.0

Model Summary

Table with columns: Property, Value
Property	Value
Model name	Fawen
Version	1.0
Developer	David Zhang
Architecture	Sparse Mixture-of-Experts (MoE)
Parameters	~35B total, ~3B active per token
Precision	Aggressively quantized, then retrained (quantization-aware)
Context window	Extended long-context
Languages	English, Chinese (multilingual capable)
Primary strengths	Tool / function calling, transparent reasoning, agentic workflows
License	Apache 2.0

Key Highlights

Sparse efficiency. Only a fraction of the 35B parameters fires on each token, so inference cost tracks a ~3B-class model while keeping the capacity of a much larger one.
Compressed without forgetting. An aggressive post-training quantization pass was followed by a retraining pass that rebuilt lost capability — smaller to host, still capable to use.
Born to use tools. Trained on a large corpus of real-world agent episodes and a curated tool-calling set spanning web access, file reading, computation, and database querying.
Thinks out loud, cleanly. A built-in reasoning scaffold separates deliberation from action, so the model plans before it acts and is easy to inspect.
Multilingual & long-context. Strong Chinese/English understanding with an extended context window for long documents and multi-turn agent sessions.

Training Approach

Fawen was adapted from a 35B sparse-MoE backbone through three coordinated efforts:

Aggressive quantization + retraining. The backbone was heavily compressed, then retrained under the quantized regime. This shrinks memory and compute footprint while preserving the model's reasoning and knowledge.
Large-scale agentic corpus. A substantial dataset of authentic agent interaction traces — multi-turn episodes containing tool invocations interleaved with step-by-step reasoning — was used to instill robust, realistic tool-use behavior.
Curated tool-calling supervision. A dedicated collection of function/tool calls across diverse APIs (web fetch, file I/O, calculator, SQL/database, and more) was added to broaden and harden calling accuracy.
Reasoning-scaffold augmentation. Training examples were framed with an explicit think-then-act structure, teaching the model to emit a reasoning block before any tool call or final answer.

The result is a model that is deployment-friendly (quantized, MoE-sparse) yet behavior-rich (agentic, transparent, multilingual).

Capabilities

Table with columns: Capability, Description
Capability	Description
Tool / Function calling	Emits well-formed tool calls for web, files, math, databases, and general APIs.
Transparent reasoning	Produces an explicit reasoning trace before answering or acting.
Agentic workflows	Sustains multi-step plans with intermediate tool use and self-correction.
Efficient inference	MoE sparsity + quantization keep active compute and memory low.
Multilingual	Fluent in English and Chinese; general multilingual coverage.
Knowledge & skills	Inherits broad knowledge, math, and coding ability from the MoE backbone.

Reasoning & Tool-Use Scaffold

Fawen structures its responses with two clear blocks:

<think> ... </think> — the model's internal deliberation: restating the goal, decomposing the problem, and deciding on a plan.
<tool_call> ... </tool_call> — a structured invocation of a tool/function, emitted only when external action is needed.

Example interaction

markdown
User: What's the weather in Shanghai right now, and should I bring an umbrella?

Assistant:
<think>
The user wants the current weather in Shanghai and a recommendation.
I need a live observation, so I should call the weather tool, then judge rain probability.
</think>
<tool_call>
{
  "name": "get_weather",
  "arguments": { "city": "Shanghai", "units": "metric" }
}
</tool_call>

[tool returns: Shanghai, 18°C, light rain, 70% humidity]

<think>
It is currently raining in Shanghai, so an umbrella is advisable.
</think>
Yes — it's lightly raining in Shanghai right now (18°C). I'd bring an umbrella.

This design makes the model's decisions auditable and lets downstream systems parse tool calls deterministically.

Evaluation

Table with columns: Agentic Coding, Fawen-1.0-35B, Qwen3.5-35B, Qwen3.6-35B, Gemma4-31B, Qwen3.5-397B
Agentic Coding	Fawen-1.0-35B	Qwen3.5-35B	Qwen3.6-35B	Gemma4-31B	Qwen3.5-397B
Terminal-Bench 2.1 (Terminus-2)	TBD	41.4	52.5	42.1	53.5
Terminal-Bench 2.1 (Claude Code)	TBD	38.9	49.2	-	48.6
SWE-bench Verified

Intended Use

In scope: assistant and agent applications that require tool use, multi-step planning, and explainable reasoning; Chinese/English conversational products; on-prem or cost-sensitive deployments thanks to the quantized MoE design.
Out of scope: high-stakes decisions without human oversight; tasks requiring guaranteed factual correctness without verification; any use that violates applicable law or safety norms.

Limitations

As a compressed model, rare knowledge or rare-language accuracy may trail a full-precision backbone; verify critical outputs.
Tool schemas must be provided at inference time; the model follows the schema rather than inventing APIs.
Long-context behavior should be validated for your specific document lengths.
License and commercial-use terms are to be confirmed by the developer.

How to Use

Load with 🤗 Transformers:

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DavidZhang/Fawen-1.0"   # replace with your repo id
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",        # quantized weights load natively
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Check the status of order #88231 and summarize it."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(out[0], skip_special_tokens=False))

For production serving, export to GGUF (CPU/RAM-friendly, llama.cpp) or vLLM (PagedAttention, low-latency) — both preserve the <think> / <tool_call> scaffold.

Citation

bibtex
@misc{fawen2026,
  title        = {Fawen: A Reasoning-Enhanced, Tool-Native Mixture-of-Experts Language Model},
  author       = {Zhang, David},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/DavidZhang/Fawen-1.0}},
  note         = {35B sparse MoE, quantized + retrained; agentic and reasoning-scaffold training.}
}

Acknowledgements

Thanks to the open agent-trajectory and tool-use research community whose public datasets and scaffolding ideas informed this model's training recipe.

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