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Jabr7
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Model Details
Model Provider
Jabr7
Model Tree
Base
unsloth/Qwen3-4B-Instruct-2507
Adapter
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
This repo ships two GGUF variants of the same project, trained on different data mixes. They trade style intensity for conversational ability:
chat (recommended) | raw | |
|---|---|---|
| Training data | single-turn pairs + 1.3k real multi-turn comment chains | single-turn post/comment pairs only |
| Multi-turn chat | works | collapses after the first reply |
| Style intensity (single-shot) | strong | strongest |
| Best for | chatting in Ollama / LM Studio | one-shot forum-style answers |
In a pairwise LLM-judge eval both variants beat the base model on subreddit-style fidelity (chat: 7-1, raw: 6-2), while the raw variant wins head-to-head on single-shot style but returns empty or off-distribution replies from the second turn onwards.
Chat variant (recommended):
bash
ollama run hf.co/Jabr7/charruadevs-4b:charruadevs-4b-chat.Q4_K_M.gguf
Raw variant (single-shot only, phrase your message like a forum post and reset the session between questions):
bash
ollama run hf.co/Jabr7/charruadevs-4b:charruadevs-4b-raw.Q4_K_M.gguf
Suggested sampling for both: temperature=0.7, top_p=0.9, repeat_penalty=1.1. The models were trained without a system prompt, so leave the system field empty for best results.
The adapter_model.safetensors in this repo is the chat variant.
python
from unsloth import FastLanguageModelmodel, tok = FastLanguageModel.from_pretrained("Jabr7/charruadevs-4b", load_in_4bit=True)FastLanguageModel.for_inference(model)msgs = [{"role": "user", "content": "¿Qué opinan de Genexus?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")out = model.generate(ids, max_new_tokens=200, temperature=0.7, top_p=0.9)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
unsloth/Qwen3-4B-Instruct-2507q_proj, k_proj, v_proj, o_proj, r=32, alpha=64This is a style and opinion model, not a factual source. It can confidently make up salaries, dates, statistics, and other numbers, so do not trust them. It also reproduces the humor, writing quirks, and biases of the forum. Intended for demonstration and entertainment.