GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
March07
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
March07
Model Tree
Qwen/Qwen2.5-Coder-32B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Trajectories are multi-turn bash-agent rollouts. Tool calls are emitted as
plain text inside the assistant turn using a <tool_call>{"name": "bash", ...}</tool_call>
protocol defined in the system prompt (not native function calling), and tool
output returns in a tool role. Loss is computed on assistant turns only.
Full-parameter SFT via LLaMA-Factory, DeepSpeed ZeRO-3, BF16, on 8x B200.
| hyperparameter | value |
|---|---|
| learning rate | 5e-6 |
| lr scheduler | cosine, warmup ratio 0.1 |
| weight decay | 0.0 |
| optimizer | AdamW |
| epochs | 5 |
| per-device batch size | 1 |
| gradient accumulation | 8 |
| effective batch size | 64 |
| max sequence length | 20,000 |
| precision | bf16 |
| attention | sdpa |
Final train loss 0.2652 (from 0.9019 at step 5); mean train loss 0.3865 over 160 steps / 5 epochs. Runtime 8,812s.
Note: 5.3% of samples exceed the 20,000-token cutoff and are truncated.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "March07/Qwen2.5-Coder-32B-terminal-agent-sft-mix2040"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")messages = [{"role": "system", "content": "You are an expert technical assistant with access to bash tools."},{"role": "user", "content": "List the files in /workspace."},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=512)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))