Instructions to use Weyaxi/Einstein-v4-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/Einstein-v4-7B with Transformers:

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

pipe = pipeline("text-generation", model="Weyaxi/Einstein-v4-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Einstein-v4-7B")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/Einstein-v4-7B")
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 Weyaxi/Einstein-v4-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/Einstein-v4-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Einstein-v4-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Weyaxi/Einstein-v4-7B

SGLang

How to use Weyaxi/Einstein-v4-7B 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 "Weyaxi/Einstein-v4-7B" \
    --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": "Weyaxi/Einstein-v4-7B",
		"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 "Weyaxi/Einstein-v4-7B" \
        --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": "Weyaxi/Einstein-v4-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Weyaxi/Einstein-v4-7B with Docker Model Runner:
```
docker model run hf.co/Weyaxi/Einstein-v4-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🔬 Einstein-v4-7B

This model is a full fine-tuned version of mistralai/Mistral-7B-v0.1 on diverse datasets.

This model is finetuned using 7xRTX3090 + 1xRTXA6000 using axolotl.

This model's training was sponsored by sablo.ai.

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: data/merged_all.json
    ds_type: json
    type: alpaca
    conversation: chatml

  - path: data/capybara_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml

  - path: data/synthia-v1.3_sharegpt_12500.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml

  - path: data/slimorca_dedup_filtered_95k_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

  - path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json
    ds_type: json
    type: sharegpt
    conversation: chatml  

dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./Einstein-v4-model

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: Einstein
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v4-7B

save_safetensors: true

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1.5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2 # changed
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 4
debug:

deepspeed: zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "<|im_end|>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"

resume_from_checkpoint: Einstein-v4-model/checkpoint-521

💬 Prompt Template

You can use this prompt template while using the model:

ChatML

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>

This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [
    {"role": "system", "content": "You are helpful AI asistant."},
    {"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

🔄 Quantizationed versions

Quantizationed versions of this model is available.

GGUF @LoneStriker

https://huggingface.co/LoneStriker/Einstein-v4-7B-GGUF

AWQ @solidrust

https://huggingface.co/solidrust/Einstein-v4-7B-AWQ

Exl2 @bartowski:

https://huggingface.co/bartowski/Einstein-v4-7B-exl2

🎯 Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.62
AI2 Reasoning Challenge (25-Shot)	64.68
HellaSwag (10-Shot)	83.75
MMLU (5-Shot)	62.31
TruthfulQA (0-shot)	55.15
Winogrande (5-shot)	76.24
GSM8k (5-shot)	57.62

🎯 Open LLM Leaderboard v2 Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	16.73
IFEval (0-Shot)	47.08
BBH (3-Shot)	14.30
MATH Lvl 5 (4-Shot)	1.74
GPQA (0-shot)	4.25
MuSR (0-shot)	19.02
MMLU-PRO (5-shot)	13.99

📚 Some resources, discussions and reviews aboout this model

🐦 Announcement tweet:

https://twitter.com/Weyaxi/status/1765851433448944125

🔍 Reddit post in r/LocalLLaMA:

https://www.reddit.com/r/LocalLLaMA/comments/1b9gmvl/meet_einsteinv47b_mistralbased_sft_model_using/

▶️ Youtube Videos

🤖 Additional information about training

This model is full fine-tuned for 1.5 epoch.

Total number of steps was 1562.

Loss graph

🤝 Acknowledgments

Thanks to sablo.ai for sponsoring this model.

Thanks to all the dataset authors mentioned in the datasets section.

Thanks to axolotl for making the repository I used to make this model.

Thanks to all open source AI community.

If you would like to support me:

☕ Buy Me a Coffee

Downloads last month: 92

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Weyaxi/Einstein-v4-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

Finetunes

2 models

Merges

16 models

Quantizations

4 models

Datasets used to train Weyaxi/Einstein-v4-7B

Collection including Weyaxi/Einstein-v4-7B

Einstein Models

Collection

Einstein series • 11 items • Updated Dec 3, 2025 • 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

64.680
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.750
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.310
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

55.150
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

76.240
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

57.620
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

47.080
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

14.300
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

1.740
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

4.250
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

19.020
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

13.990