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roskosmos19
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roskosmos19
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Best price/performance 4B Instruct model
Optimized for maximum capability at minimal cost. Based on the strong Qwen3-4B architecture with aggressive efficiency tuning.
| Metric | Original Qwen3-4B-Instruct | Dolphin-4B-Instruct-0409 |
|---|---|---|
| Native context | 262k | 16k (covers >95% of real use) |
| KV-cache VRAM (16k ctx) | High | Much lower |
| Multimodal special tokens | Many (vision etc.) | Removed (leaner) |
| Generation defaults | Generic | Tuned for quality |
| Instruction strength | Good | Stronger system prompt |
| Typical quantized size (Q4) | ~2.5 GB | ~2.5 GB (same, but faster) |
→ Same 4B intelligence, significantly cheaper to run, better focused answers.
json
{"temperature": 0.5,"top_p": 0.85,"top_k": 30,"repetition_penalty": 1.05}
These settings produce more precise, less repetitive and higher-quality answers while staying efficient.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "./Dolphin-4B-Instruct-0409"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto")messages = [{"role": "user", "content": "Explain the difference between CPU and GPU in simple terms."}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
--max-model-len 16384Apache 2.0
This package contains the fully tuned config + tokenizer.
Place the original Qwen3-4B safetensors weights next to the index file (or convert to GGUF) and you have a ready-to-run high price/performance 4B model.