Instructions to use opencerebral/Boris-1.3-125M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opencerebral/Boris-1.3-125M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="opencerebral/Boris-1.3-125M-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opencerebral/Boris-1.3-125M-Instruct")
model = AutoModelForCausalLM.from_pretrained("opencerebral/Boris-1.3-125M-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use opencerebral/Boris-1.3-125M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opencerebral/Boris-1.3-125M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencerebral/Boris-1.3-125M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/opencerebral/Boris-1.3-125M-Instruct

SGLang

How to use opencerebral/Boris-1.3-125M-Instruct 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 "opencerebral/Boris-1.3-125M-Instruct" \
    --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": "opencerebral/Boris-1.3-125M-Instruct",
		"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 "opencerebral/Boris-1.3-125M-Instruct" \
        --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": "opencerebral/Boris-1.3-125M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use opencerebral/Boris-1.3-125M-Instruct with Docker Model Runner:
```
docker model run hf.co/opencerebral/Boris-1.3-125M-Instruct
```

KlondikeDev commited on about 1 month ago

Commit

5a22b97

verified ·

1 Parent(s): abe5ed1

Upload folder using huggingface_hub

Browse files

Files changed (6) hide show

README.md +40 -0
config.json +35 -0
generation_config.json +9 -0
model.safetensors +3 -0
tokenizer.json +0 -0
tokenizer_config.json +14 -0

README.md ADDED Viewed

	@@ -0,0 +1,40 @@

+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- gpt2
+- boris
+---
+# boris-1-125M-first-run
+A 12-layer, 768-dim GPT-2-tier (~125M parameter) language model
+trained from scratch with the Boris-1 pipeline, exported to `GPT2LMHeadModel`.
+Exported from checkpoint `best.pt`.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("<user>/boris-1-125M")
+model = AutoModelForCausalLM.from_pretrained("<user>/boris-1-125M")
+ids = tok("The capital of France is", return_tensors="pt").input_ids
+print(tok.decode(model.generate(ids, max_new_tokens=40)[0]))
+```
+## Details
+| | |
+|---|---|
+| Architecture | GPT-2 (pre-LN, learned positional embeddings, tied embeddings) |
+| Layers / heads / d_model | 12 / 12 / 768 |
+| Context length | 1024 |
+| Vocab | 50304 (GPT-NeoX-20B BPE, padded) |
+| Tokenizer | `EleutherAI/gpt-neox-20b` |
+| Precision | trained in bf16 autocast with fp32 master weights |
+Base model; not instruction-tuned or aligned. Outputs may be inaccurate or
+offensive.

config.json ADDED Viewed

	@@ -0,0 +1,35 @@

+{
+  "activation_function": "gelu",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 0,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": null,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 50304
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.13.1",
+  "use_cache": true
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a92df0dbaf90b779f78a38b8a7c79d3e3db4be2224a40e9b2c41724514c046cc
+size 497918592

tokenizer.json ADDED Viewed

The diff for this file is too large to render. See raw diff

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,14 @@

+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|padding|>",
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<|endoftext|>"
+}