Weights of Model Trained Via Unsloth

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README.md

@@ -1,3 +1,148 @@
----
-license: mit
----
+---
+base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+model_name: methanol-apc
+tags:
+  - base_model:adapter:unsloth/Qwen2.5-3B-Instruct-bnb-4bit
+  - grpo
+  - lora
+  - peft
+  - trl
+  - unsloth
+  - reinforcement-learning
+  - process-control
+  - methanol
+license: apache-2.0
+---
+
+# Model Card for methanol-apc
+
+LoRA adapter for [`unsloth/Qwen2.5-3B-Instruct-bnb-4bit`](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit), fine-tuned with **GRPO** ([Group Relative Policy Optimization](https://huggingface.co/papers/2402.03300)) using [Unsloth](https://github.com/unslothai/unsloth) to act as an autonomous **Advanced Process Control (APC)** operator for a methanol synthesis reactor.
+
+The agent reads simulated sensor readings (temperature, pressure, H₂/CO ratio, catalyst health, …) and emits a JSON control action — feed rates, cooling water flow, and compressor power — that is scored by the [`methanol-apc` OpenEnv environment](https://huggingface.co/spaces/glitchfilter/methanol-apc-env).
+
+- **Model on Hugging Face:** [glitchfilter/methanol-apc](https://huggingface.co/glitchfilter/methanol-apc)
+- **Environment:** [glitchfilter/methanol-apc-env (HF Space)](https://huggingface.co/spaces/glitchfilter/methanol-apc-env) · [Bhavneet1492/openenv-methanol-apc (GitHub)](https://github.com/Bhavneet1492/openenv-methanol-apc)
+- **Base model:** [unsloth/Qwen2.5-3B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit)
+
+## Quick start
+
+```python
+from unsloth import FastLanguageModel
+from peft import PeftModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+model = PeftModel.from_pretrained(model, "glitchfilter/methanol-apc")
+FastLanguageModel.for_inference(model)
+
+system_prompt = (
+    "You are an AI controller for a methanol synthesis reactor. "
+    "Output a JSON control action with fields: "
+    '{"feed_rate_h2": <0-10>, "feed_rate_co": <0-5>, '
+    '"cooling_water_flow": <0-100>, "compressor_power": <0-100>}.'
+)
+sensors = "T=248.3°C P=85.0bar H2=4.50mol/s CO=2.20mol/s ratio=2.05 cool=55L/min cat_health=98%"
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user",   "content": f"Current sensor readings:\n{sensors}\n\nProvide control action as JSON:"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+import torch
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=128, temperature=0.3, do_sample=True,
+                         pad_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Training procedure
+
+Trained with **GRPO** accelerated by Unsloth's 4-bit quantized base model and LoRA adapters.
+
+**Pipeline:** `LLM generates JSON action` → `reward fn parses & scores` → `env.step()` → `multi-component reward` → `GRPO update`.
+
+### Key design choices
+
+- **Curriculum learning** over three task types:
+  - `startup` (40%) — easy: ramp reactor to operating temperature
+  - `optimization` (35%) — medium: maximize profit at steady state
+  - `disturbance_rejection` (25%) — hard: handle cooling system failures
+- **Multi-component reward** combining:
+  1. Physics reward from `env.step` (× 0.55)
+  2. Format-compliance bonus for valid JSON actions (+0.10)
+  3. Action-quality score grounded in stoichiometry / cooling adequacy ([−0.30, +0.20])
+  4. 3-step lookahead penalty to surface delayed thermal-runaway consequences ([−0.20, 0])
+- **Deterministic replay**: each prompt stores `(task, seed, num_warmup)` so all GRPO group completions evaluate against an identical environment state.
+
+### Hyperparameters
+
+| | |
+|---|---|
+| Base model | `unsloth/Qwen2.5-3B-Instruct-bnb-4bit` (4-bit) |
+| LoRA `r` / `alpha` / dropout | 16 / 32 / 0 |
+| LoRA target modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
+| Max sequence length | 2048 |
+| Max completion length | 120 tokens |
+| Train steps | 200 |
+| Per-device batch × grad accum | 2 × 4 |
+| GRPO group size (`num_generations`) | 8 |
+| Learning rate | 5e-6 |
+| Warmup ratio | 0.05 |
+| Max grad norm | 1.0 |
+| Sampling temperature | 0.7 |
+| KL coefficient | 0.05 |
+| Precision | fp16 (bf16 where supported) |
+| Gradient checkpointing | Unsloth |
+| Prompt dataset size | 300 |
+
+### Framework versions
+
+- PEFT 0.18.1
+- Unsloth (`git+https://github.com/unslothai/unsloth.git`)
+- TRL ≥ 0.15
+- `openenv-core[core]` ≥ 0.2.2
+
+## Evaluation
+
+The trained agent is compared against a random-action baseline on the `optimization` task (5 episodes × 15 steps). Plots are produced by the training notebook and saved to [plots/](plots/):
+
+| Plot | File |
+|---|---|
+| Training loss | [plots/loss_curve.png](plots/loss_curve.png) |
+| Reward per step (trained) | [plots/reward_curve.png](plots/reward_curve.png) |
+| Baseline vs trained | [plots/baseline_vs_trained.png](plots/baseline_vs_trained.png) |
+
+## Intended use & limitations
+
+This adapter is a **research artifact** demonstrating GRPO-based fine-tuning for closed-loop chemical-process control on a *simulated* environment. It is **not** suitable for, and must not be deployed against, any real industrial reactor or safety-critical system. The simulator is a simplified model of methanol synthesis (ICI low-pressure process, Cu/ZnO/Al₂O₃ catalyst) and does not capture the full dynamics, instrumentation, or failure modes of a physical plant.
+
+## Citations
+
+GRPO:
+
+```bibtex
+@article{shao2024deepseekmath,
+  title  = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+  author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+  year   = {2024},
+  eprint = {arXiv:2402.03300}
+}
+```
+
+Unsloth:
+
+```bibtex
+@software{unsloth2024,
+  title  = {{Unsloth: 2x faster, 50\% less memory LLM finetuning}},
+  author = {Daniel Han and Michael Han and {Unsloth team}},
+  url    = {https://github.com/unslothai/unsloth},
+  year   = {2024}
+}
+```

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "down_proj",
+    "gate_proj",
+    "q_proj",
+    "v_proj",
+    "up_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

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tokenizer_config.json

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+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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