Instructions to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos

SGLang

How to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos with Docker Model Runner:
```
docker model run hf.co/EpistemeAI2/Fireball-Mistral-Nemo-12B-Philos
```

legolasyiu commited on Sep 4, 2024

Commit

a2905ab

verified ·

1 Parent(s): 20b20bc

Update README.md

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@@ -11,6 +11,37 @@ tags:
 - trl
 ---
 # Uploaded  model
 - **Developed by:** EpistemeAI

 - trl
 ---
+# Fireball-Mistral-Nemo-12B-Philos
+Supervised Fined tuned by dataset of philosophy, math, coding and languages.
+# Original Model Card
+# Model Card for Mistral-Nemo-Instruct-2407
+The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407). Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
+For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-nemo/).
+## Key features
+- Released under the **Apache 2 License**
+- Pre-trained and instructed versions
+- Trained with a **128k context window**
+- Trained on a large proportion of **multilingual and code data**
+- Drop-in replacement of Mistral 7B
+## Model Architecture
+Mistral Nemo is a transformer model, with the following architecture choices:
+- **Layers:** 40
+- **Dim:** 5,120
+- **Head dim:** 128
+- **Hidden dim:** 14,336
+- **Activation Function:** SwiGLU
+- **Number of heads:** 32
+- **Number of kv-heads:** 8 (GQA)
+- **Vocabulary size:** 2**17 ~= 128k
+- **Rotary embeddings (theta = 1M)**
 # Uploaded  model
 - **Developed by:** EpistemeAI