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March07
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Model Details
Model Provider
March07
Model Tree
Qwen/Qwen2.5-Coder-32B-Instruct
Input Modalities
Output Modalities
Supported Functionality
These runs study whether the gain from lowering the trajectory-inclusion threshold comes from data quality or data quantity. Lowering the threshold changes both at once (2,040 -> 3,291 trajectories, +61%), so the arms below hold N fixed and vary only composition.
| arm | N | fully-passing | imperfect | imperfect % |
|---|---|---|---|---|
| A (all-pass baseline) | 2,040 | 2,040 | 0 | 0% |
| B (size-matched mix) | 2,040 | 1,255 | 785 | 38% |
| C (all-pass subset of B) |
| 1,255 |
| 1,255 |
| 0 |
| 0% |
| D-dose20 | 2,040 | 1,632 | 408 | 20% |
| R (full, lowest threshold) | 3,291 | 2,040 | 1,251 | 38% |
This model: 1,255 trajectories, all fully-passing (0% imperfect).
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); 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 0.3247 (from 0.9182 at the first logged step); mean train loss 0.4421 over 100 steps / 5 epochs. Runtime 1.54h.
Note: a small fraction of samples (~5%) exceed the 20,000-token cutoff and are truncated.
Loss is not comparable across arms: arms containing imperfect trajectories are fitting partly-failing rollouts, so a lower loss does not imply a better agent. These checkpoints must be compared by task success rate, not by training loss.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "March07/Qwen2.5-Coder-32B-terminal-agent-sft-allpass1255"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))