Text Generation
Transformers
Safetensors
mistral
Generated from Trainer
trl
hf_jobs
sft
text-generation-inference
Instructions to use misterJB/atlas-field-528hz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use misterJB/atlas-field-528hz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="misterJB/atlas-field-528hz")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("misterJB/atlas-field-528hz") model = AutoModelForCausalLM.from_pretrained("misterJB/atlas-field-528hz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use misterJB/atlas-field-528hz with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "misterJB/atlas-field-528hz" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "misterJB/atlas-field-528hz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/misterJB/atlas-field-528hz
- SGLang
How to use misterJB/atlas-field-528hz 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 "misterJB/atlas-field-528hz" \ --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": "misterJB/atlas-field-528hz", "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 "misterJB/atlas-field-528hz" \ --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": "misterJB/atlas-field-528hz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use misterJB/atlas-field-528hz with Docker Model Runner:
docker model run hf.co/misterJB/atlas-field-528hz
Training in progress, step 500, checkpoint
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| 102 |
{
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| 103 |
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| 104 |
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|
| 105 |
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|
| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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|
| 111 |
}
|
| 112 |
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|
| 113 |
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|
| 114 |
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"max_steps": 2560,
|
| 115 |
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|
| 116 |
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"num_train_epochs": 2,
|
| 117 |
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|
| 118 |
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| 119 |
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|
| 127 |
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| 128 |
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|
| 129 |
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| 130 |
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"total_flos": 1.4446972723750502e+17,
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| 132 |
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| 133 |
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last-checkpoint/training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5713
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:98f86bcfd098aae8e261cc44af568a1b790f321588e8fdcc26b68725adda0314
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| 3 |
size 5713
|