Text Generation
Transformers
Safetensors
English
llama
causal-lm
pretraining
from-scratch
single-gpu
nvidia-l20
text-generation-inference
Instructions to use Epode/Prelude-1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Epode/Prelude-1B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Epode/Prelude-1B-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Epode/Prelude-1B-Base") model = AutoModelForCausalLM.from_pretrained("Epode/Prelude-1B-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Epode/Prelude-1B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Epode/Prelude-1B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Epode/Prelude-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Epode/Prelude-1B-Base
- SGLang
How to use Epode/Prelude-1B-Base 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 "Epode/Prelude-1B-Base" \ --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": "Epode/Prelude-1B-Base", "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 "Epode/Prelude-1B-Base" \ --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": "Epode/Prelude-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Epode/Prelude-1B-Base with Docker Model Runner:
docker model run hf.co/Epode/Prelude-1B-Base
Li commited on
Upload exact final 20B-token base checkpoint
Browse files- config.json +30 -0
- generation_config.json +5 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +209 -0
- model_config.yaml +53 -0
- release-manifest.json +58 -0
- special_tokens_map.json +16 -0
- tokenizer.json +0 -0
- tokenizer_config.json +29 -0
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "float32",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pad_token_id": 1,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"pad_token_id": 1,
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"transformers_version": "4.56.2"
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}
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1625415432
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model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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model.safetensors.index.json
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model_config.yaml
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@@ -0,0 +1,53 @@
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attention_logit_softcapping: null
|
| 2 |
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attention_scores_scalar: null
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attn_bias: false
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bias: false
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block_size: 2048
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final_logit_softcapping: null
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first_k_dense_replace: null
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gelu_approximate: none
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head_size: 64
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hf_config:
|
| 11 |
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name: TinyLlama-1.1B-intermediate-step-1431k-3T
|
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org: TinyLlama
|
| 13 |
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intermediate_size: 5632
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latent_attention: null
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lm_head_bias: false
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mlp_class_name: LLaMAMLP
|
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moe_intermediate_size: null
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n_embd: 2048
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n_expert: 0
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| 20 |
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n_expert_groups: null
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n_expert_per_token: 0
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n_head: 32
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n_layer: 22
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| 24 |
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n_query_groups: 4
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n_shared_expert: null
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| 26 |
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n_topk_groups: null
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| 27 |
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n_topk_scores_per_group: null
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| 28 |
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name: tiny-llama-1.1b
|
| 29 |
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norm_1: true
|
| 30 |
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norm_2: true
|
| 31 |
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norm_class_name: RMSNorm
|
| 32 |
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norm_eps: 1.0e-05
|
| 33 |
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norm_qk: false
|
| 34 |
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norm_qk_type: default
|
| 35 |
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norm_topk_prob: false
|
| 36 |
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padded_vocab_size: 32000
|
| 37 |
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padding_multiple: 64
|
| 38 |
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parallel_residual: false
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| 39 |
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post_attention_norm: false
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| 40 |
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post_mlp_norm: false
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rope_adjustments: null
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| 42 |
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rope_base: 10000
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rope_condense_ratio: 1
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rope_indices: null
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rope_interleave: false
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rope_local_base_freq: null
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rotary_percentage: 1.0
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routed_scaling_factor: 1.0
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scale_embeddings: false
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shared_attention_norm: false
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sliding_window_indices: null
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sliding_window_size: null
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vocab_size: 32000
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release-manifest.json
ADDED
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@@ -0,0 +1,58 @@
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special_tokens_map.json
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@@ -0,0 +1,16 @@
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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|
| 1 |
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