🧠 Qwen3.8-27B-AgentCoder

A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning

Author: Bruno Pistone
Repository: br1-pist/Qwen3.8-27B-AgentCoder
License: Apache 2.0
Base model: Qwen/Qwen3.8-27B

Model Description

Qwen3.8-27B-AgentCoder is a fine-tuned version of the Qwen/Qwen3.8-27B model, optimized for:

🧮 Complex reasoning tasks
🧰 Tool calling
💻 Code generation

The model was developed through sequential fine-tuning, followed by a Direct Preference Optimization (DPO) post-training stage to improve alignment, coherence, and reasoning accuracy.

Highlights

Post-trained with DPO using chosen/rejected pairs for better alignment
Excellent balance between tool use, code generation, and reasoning

🚀 Direct Use

Qwen3.8-27B-AgentCoder can be used directly for:

✅ Tool calling in complex reasoning tasks
✅ Code generation for Python, JS, and other languages
✅ Multi-domain reasoning (math, logic, Q&A)

⚠️ Out-of-Scope Use

❌ Highly sensitive or confidential data
❌ Domains requiring expert-level specialization
❌ Tasks where full explainability is mandatory

💻 Getting Started

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "br1-pist/Qwen3.8-27B-AgentCoder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
prompt = "Give me a short introduction to large language models."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(output)

🧠 Training Details

Training Procedure

Phase 1 — Post-Training: Reinforcement Learning from AI Feedback (RLAIF) with Group Relative Policy Optimization (GRPO)

After sequential fine-tuning and preference alignment, the model underwent a GRPO phase in which an LLM judge — not a static preference dataset — supplied the reward signal. For each prompt the policy samples a group of completions, the judge scores each one against a rubric, and the advantage is computed relative to the group mean, so no value network is required.

Reward model (RLAIF)

Judge: moonshotai/Kimi-K3 via the Amazon Bedrock Converse API
Rubric-scored 0–10, normalised to 0–1; rewards evidence-gathering, correct tool choice, root-cause reasoning and verification, and penalises speculation, wrong-tool calls and symptom patching
Unscorable completions (content filter, malformed verdict) are excluded from the group baseline rather than scored zero

GRPO parameters

Generations per prompt (group size): 8
Beta (KL coefficient): 0.04
Loss type: dapo
Reward scaling: group (unit variance within each group)
Sampling temperature: 1.0
Max completion length: 640 tokens, truncated completions masked out of the loss

Training parameters

Learning rate: 1e-5, cosine schedule with a 10% floor
Warmup steps: 46
Per-device batch size: 2 × 16 GPUs × gradient accumulation 4 = 128 completions (16 prompts) per step
Epochs: 1 → 230 optimiser steps
Max grad norm: 0.5
LoRA: rank 16, alpha 32, dropout 0.1, applied to both the full-attention (q/k/v/o_proj) and linear-attention (in_proj_qkv, in_proj_z, out_proj) projections
Precision: bf16, FSDP full-shard
Hardware: 2 × ml.p5.48xlarge (16 × H100), 21h47m wall clock

GRPO Data

3,686 multi-turn, tool-call conversations (87% multi-turn; prompts left untruncated)
Coding, general Q&A, data management, bash execution

Objective

Improve clarity, correctness, and helpfulness
Reduce hallucinations and verbosity

Outcome

Mean judge reward rose from 0.615 (first 25 steps) to 0.689 (final 25), +12.0%
Mean completion length and truncation rate both fell, i.e. the gain came from concision, not padding
Policy KL from the reference model rose from ~2.6e-4 to ~8.0e-3, confirming the policy actually moved

🖥️ Technical Specifications

Model Architecture

Model type: Causal language model
Parameters: 4.0B
Context length: ~264K tokens
Thinking mode: Enabled

Compute Infrastructure

Hardware

GPU: NVIDIA H100 (80 GB VRAM)
System RAM: 2 TiB
Memory per vCPU: 10.67 GiB

Software

Python: 3.12
Transformers: 5.3.0
Libraries: bitsandbytes, safetensors, torch, trl, scikit-learn, tokenizers, psutil, py7zr

🧾 Citation

BibTeX

@article{qwen3.5-4b-thinking-2507-toolcode,
  title={Qwen3.8-27B-AgentCoder: A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning},
  author={Bruno Pistone},
  year={2025},
  journal={Hugging Face Model Hub}
}

APA

Bruno Pistone. (2026). Qwen3.8-27B-AgentCoder: A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning. Hugging Face Model Hub. https://huggingface.co/br1-pist/Qwen3.8-27B-AgentCoder