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Fox-1.5
Fox-AI-by-teolm30/Fox-1.5
Fox-1.5 is a text generation model from Fox-AI-by-teolm30. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
All-time downloads
680
Public
Parameters
7.6B
13.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.gguf7.8 GB · 58%
Parameter types
How the weights are stored.
I326.5B · 86%
Try a prompt
Base models
Model card
From the Hugging Face model README
🦊 Fox 1.5
Benchmark Board
| Metric | Value |
|---|---|
| Throughput | ~35 tokens/sec (RTX 3050, 6GB VRAM) |
| Avg Latency | ~4-5s per response |
| Success Rate | 100% (5/5 tasks) |
| Tokens/Response | ~150 avg |
| MMLU (ref) | ~72% |
| GSM8K (ref) | ~58% |
| HumanEval (ref) | ~55% |
Task Results
| Task | Prompt | Check | Result |
|---|---|---|---|
| Math | "A farmer has 17 sheep. All but 9 run away. How many sheep left?" | 9 | ✅ |
| Coding | "Write a Python function to check if a number is prime." | def | ✅ |
| Knowledge | "What is the capital of Greece?" | athens | ✅ |
| Logic | "If all cats are animals and some animals are pets, then some cats are pets. True or false?" | true | ✅ |
| Translation | "Translate to Greek: Hello, how are you?" | γεια | ✅ |
Quick Facts
| Property | Value |
|---|---|
| Base Model | Qwen2.5-7B-Instruct |
| Quantization | GPTQ 4-bit |
| Parameters | 7B |
| Context Length | 32K tokens |
| Size | 5.3GB |
| VRAM Required | ~6GB |
| License | Apache 2.0 |
Capabilities
- Text & Chat — multilingual conversations, creative writing
- Coding — Python, JavaScript, C++, Rust, Go, 50+ languages
- Reasoning — math, logic, step-by-step problem solving
- Agentic Use — tool calling, function execution, OpenClaw compatible
Run it
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "teolm30/Fox-1.5"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [{"role": "user", "content": "Explain quantum entanglement in simple terms"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda:0")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
For 4-bit GPTQ loading: pip install auto-gptq optimum
Limitations
- Text-only (no vision in base form)
- Image generation requires a separate model
Built by T_craftClaw 🔥 | Owner: teolm30
🤖 Run with Ollama
ollama run hf.co/teolm30/Fox-1.5
- Task
- Text Generation
- Type
- qwen2
- License
- apache-2.0
- Languages
- en, multilingual
- Created
- Apr 25, 2026
- Updated
- May 31, 2026