Instructions to use Kush26/ember with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kush26/ember with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kush26/ember")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Kush26/ember", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kush26/ember with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kush26/ember" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kush26/ember", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kush26/ember
- SGLang
How to use Kush26/ember 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 "Kush26/ember" \ --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": "Kush26/ember", "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 "Kush26/ember" \ --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": "Kush26/ember", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kush26/ember with Docker Model Runner:
docker model run hf.co/Kush26/ember
Ship model from checkpoint-3650 via Forge
Browse files- README.md +20 -0
- config.json +25 -0
- forge_metadata.json +10 -0
- generation_config.json +9 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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---
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license: other
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library_name: transformers
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tags:
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- forge
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- nomad-training
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---
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# ember-275m
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This model was trained using [Forge](https://github.com/your-repo/forge).
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## Training Details
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- **Final Step**: 3650
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- **Final Loss**: 1.6869081497192382
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- **Hardware**: modal
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- **Orchestration**: Nomad Training via Forge
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## Checkpoint Source
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Exported from atomic checkpoint `3650` on the `checkpoints` branch.
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config.json
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{
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"architectures": [
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"EmberForCausalLM"
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],
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"bos_token_id": 1,
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"dropout": 0.0,
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"dtype": "float32",
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_size": 1024,
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"intermediate_size": 2730,
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"max_position_embeddings": 2048,
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"model_type": "ember",
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"num_attention_heads": 16,
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"num_hidden_layers": 18,
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"num_key_value_heads": 8,
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"pad_token_id": 3,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"transformers_version": "5.9.0",
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"use_bias": false,
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"use_cache": false,
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"vocab_size": 65536
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}
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forge_metadata.json
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{
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"project_name": "ember-275m",
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"global_step": 3650,
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"total_samples": 934400,
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"loss": 1.6869081497192382,
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"learning_rate": 0.0002994020600287052,
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"epoch": 3.0050333333333334,
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"timestamp": 1780397706.2261117,
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"hardware": "modal"
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 3,
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"transformers_version": "5.9.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c8ac9117f506bbd0d5b8022111022651b5e60d5fa1fe50c46a4615dcc629328f
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size 1098930264
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a9511ba858afd96718533518d9637c166b38f489a167b2f74cda8e1dcd46bf7a
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size 5265
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