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Model: kosa-labs/kosa-4B-it-v1
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- qwen3
+- instruct
+---
+# kosa-4B-it-v1
+
+An instruction-tuned model built on Qwen/Qwen3-4B-Instruct-2507 using the Kosa Method. Kosa Labs is a UK-based independent lab.
+
+## Benchmarks
+
+| Benchmark | Qwen3-4B-Instruct-2507 | kosa-4B-it-v1 |
+|---|---:|---:|
+| GSM8K (strict) | 73.24% | 84.23% |
+| GSM8K (flexible) | 79.15% | 85.60% |
+| IFEval (prompt strict) | 83.36% | 85.77% |
+| IFEval (instruction strict) | 88.61% | 90.29% |
+| ARC-Challenge (acc_norm) | 43.09% | 52.13% |
+| MMLU | 61.89% | 65.76% |
+| **Average** | **71.56%** | **77.30%** |
+
+Both models evaluated in the same session with identical settings: lm-evaluation-harness 0.4.12, vLLM, bfloat16, temperature 0, chat template applied. Raw result files in /benchmarks. Training data verified clean against all four benchmark test sets (13-gram and 8-gram overlap checks with positive-control validation).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "kosa-labs/kosa-4B-it-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "Write a concise project update."}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+GGUF quantizations are available in /gguf:
+- gguf/kosa-4B-it-v1-Q4_K_M.gguf
+- gguf/kosa-4B-it-v1-Q5_K_M.gguf
+- gguf/kosa-4B-it-v1-Q8_0.gguf