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Model: lewisdog/qwen3-1.7b-cogs-ingest
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-1.7B
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- qwen3
+- cogs
+- json
+- structured-output
+- mlx
+---
+
+# qwen3-1.7b-cogs-ingest
+
+**Merged** full model (Qwen/Qwen3-1.7B + LoRA, weights fused, bf16) — the student
+model for the [cogs](https://github.com/trunksio/cogs) `ingest` pipeline. Use
+this repo when you want a standalone model to serve or convert; the standalone
+LoRA adapter is at
+[`lewisdog/qwen3-1.7b-cogs-ingest-lora`](https://huggingface.co/lewisdog/qwen3-1.7b-cogs-ingest-lora).
+
+It replaces a large teacher LLM as a local, OpenAI-compatible provider for four
+structured-output tasks, emitting **compact JSON exactly as the cogs runtime
+parses it**:
+
+| task            | required top-level JSON keys                |
+|-----------------|---------------------------------------------|
+| `extract`       | `summary`, `key_claims` (+ quotes/entities) |
+| `suggest_links` | `linked_claims`                             |
+| `page_update`   | `topic`, `section_md`, `relevant`           |
+| `contradiction` | `findings`                                  |
+
+## Apple silicon (MLX / omlx)
+
+```sh
+# converts + 4-bit quantizes into an MLX model dir
+python3 -m mlx_lm.convert --hf-path lewisdog/qwen3-1.7b-cogs-ingest -q --mlx-path qwen3-cogs-ingest-mlx
+# then drop into ~/.omlx/models and point cogs [llm] at it
+```
+
+## ⚠️ Serving: avoid pure greedy decoding
+
+The model learned the schema well, but under **pure greedy (temperature 0, no
+penalty)** it degenerates on the long list-valued tasks (`extract`,
+`suggest_links`): it keeps appending list items and never emits `<|im_end|>`,
+leaving valid-but-**unterminated** JSON. Fix with either:
+
+- `repetition_penalty ≈ 1.1` (keeps determinism), **or**
+- Qwen non-thinking sampling: `temperature=0.7, top_p=0.8, top_k=20`
+  (this model's bundled `generation_config.json` already samples at temp 0.6,
+  which sidesteps the issue — just don't override it to temp-0 greedy).
+
+With either, a 5-sample / 4-task JSON parse eval is **5/5 (100%) schema-exact**
+(vs. 3/5 at plain greedy). Use `enable_thinking=False` and stop on `<|im_end|>`.
+
+## Transformers usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "lewisdog/qwen3-1.7b-cogs-ingest", dtype="bfloat16", device_map="auto"
+).eval()
+tok = AutoTokenizer.from_pretrained("lewisdog/qwen3-1.7b-cogs-ingest")
+
+enc = tok.apply_chat_template(
+    messages, add_generation_prompt=True, enable_thinking=False,
+    return_tensors="pt", return_dict=True,
+).to(model.device)
+out = model.generate(**enc, max_new_tokens=2048, do_sample=False,
+                     repetition_penalty=1.1, pad_token_id=tok.pad_token_id)
+print(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Training
+
+LoRA SFT (TRL) on 1,921 `cogs distill` chat pairs, 2 epochs, effective batch 16,
+max_seq 8192, lr 1e-4 cosine, bf16, on an NVIDIA DGX Spark (GB10).
+Train loss 2.55 → 1.41; eval loss 1.125 → 1.118 (still decreasing); eval
+token-accuracy 0.756. Full details: adapter repo card + `RESULTS.md`.
+
+- PEFT 0.19.1 · TRL 1.7.1 · Transformers 5.13.0 · PyTorch 2.12.1+cu130