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mcp-horizon-support-v1
mcphorizon/mcp-horizon-support-v1
mcp-horizon-support-v1 is a machine learning model from mcphorizon. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned Qwen3-4B on MCP Horizon product documentation for question-answering and support use cases.
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At a glance
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026
- SHA
- c9dc7994
Base models
Model card
From the Hugging Face model README
mcp-horizon-support-v1
Fine-tuned Qwen3-4B on MCP Horizon product documentation for question-answering and support use cases.
Base model
Qwen/Qwen3-4B — fine-tuned with QLoRA (4-bit) via Unsloth.
Training
| Setting | Value |
|---|---|
| Method | QLoRA (LoRA rank 16, alpha 32) |
| Epochs | 5 |
| Training examples | 534 |
| Final loss | 0.7498 |
| Hardware | NVIDIA RTX 4070 12GB |
Evaluation (DeepEval, 60 examples)
| Metric | Score | Pass Rate |
|---|---|---|
| Answer Relevancy | 0.86 | 80% |
| Correctness (GEval) | 0.32 | 22% |
| Completeness (GEval) | 0.35 | 25% |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "mcphorizon/mcp-horizon-support-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
messages = [{"role": "user", "content": "How do I configure access controls in MCP Horizon?"}]
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][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Ollama
A GGUF Q4_K_M version is also available for use with Ollama/llama.cpp.
- Type
- qwen3
- License
- apache-2.0
- Languages
- en
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026