Instructions to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B with Transformers:

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

pipe = pipeline("text-generation", model="EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B 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-evol-Instruct-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B

SGLang

How to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B 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-evol-Instruct-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-evol-Instruct-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B 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-evol-Instruct-14B 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-evol-Instruct-14B 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-evol-Instruct-14B 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-evol-Instruct-14B",
    max_seq_length=2048,
)

Docker Model Runner
How to use EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B with Docker Model Runner:
```
docker model run hf.co/EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Model Card for Fireball-Mistral-Nemo-evol-Instruct-24B, fine tuned Mistral-Nemo-Instruct-2407 with merge

The EpistemeAI2's Fireball-Mistral-Nemo-Evol Instruct-24B , fine tuned Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the 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.

Original Model Card

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

How to

Wizard (recommended)

plesee use Wizard prompt

f"""Below is an instruction that describes a task. \
    Write a response that appropriately completes the request.
    ### Instruction:
    {x['instruction']}

    ### Response:
    """

Model card from Merged Model

EpistemeAI/Fireball-Mistral-Nemo-Instruct-14B-merge-v1

EpistemeAI/Fireball-Mistral-Nemo-Instruct-14B-merge-v1 is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
    - model: EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD
      layer_range: [0, 32]
  - sources:
    - model: EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Uploaded model

Developed by: EpistemeAI2
License: apache-2.0
Finetuned from model : EpistemeAI/Fireball-Mistral-Nemo-Instruct-24B-merge-v1

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: -

Model tree for EpistemeAI2/Fireball-Mistral-Nemo-evol-Instruct-14B

Base model

unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit

Finetuned

EpistemeAI2/Fireball-Mistral-Nemo-Instruct-emo-PHD

Finetuned

EpistemeAI/Fireball-Mistral-Nemo-Instruct-24B-merge-v1

Finetuned

(1)

this model

Quantizations

4 models