Instructions to use abacusai/bigyi-15b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacusai/bigyi-15b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacusai/bigyi-15b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacusai/bigyi-15b") model = AutoModelForCausalLM.from_pretrained("abacusai/bigyi-15b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use abacusai/bigyi-15b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacusai/bigyi-15b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/bigyi-15b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abacusai/bigyi-15b
- SGLang
How to use abacusai/bigyi-15b 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 "abacusai/bigyi-15b" \ --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": "abacusai/bigyi-15b", "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 "abacusai/bigyi-15b" \ --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": "abacusai/bigyi-15b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abacusai/bigyi-15b with Docker Model Runner:
docker model run hf.co/abacusai/bigyi-15b
bigyi-15b
I recently made bigstral-12b and then I saw this new awesome model yi-9b and decided to make an embiggened version.
This is a merge of pre-trained language models created using mergekit.
Bigyi-15b is a base / completion model, so there is no chat template.
It has a 4k context.
Example
Here is a recipe for Mai Tai:\n\n1:
3 parts rum, 2: 3 parts pineapple juice, 3: half a cup of lime juice, 4: 6 to 8 fresh or frozen pineapple chunks, 5: crushed ice. Mix all ingredients except ice and pour into glasses with ice. Garnish with a pineapple slice.
Here is an implementation of 2-sum in golang:
func twoSum(nums []int, target int) []int {
if len(nums) <= 1 { return nil }
m := map[int] bool{}
for i := range(nums) {{
n = nums[i]
// find the complement of current number in map
comp = target - n
if comp in m { return [m[comp], i+1 ] }
else { m[n] = true }
}}
return nil
}
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 12]
model: 01-ai/Yi-9B
- sources:
- layer_range: [6, 18]
model: 01-ai/Yi-9B
- sources:
- layer_range: [12, 24]
model: 01-ai/Yi-9B
- sources:
- layer_range: [18, 30]
model: 01-ai/Yi-9B
- sources:
- layer_range: [24, 36]
model: 01-ai/Yi-9B
- sources:
- layer_range: [30, 42]
model: 01-ai/Yi-9B
- sources:
- layer_range: [36, 48]
model: 01-ai/Yi-9B
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