How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lastmass/llama3.2-1B-chinese:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf lastmass/llama3.2-1B-chinese:Q4_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lastmass/llama3.2-1B-chinese:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf lastmass/llama3.2-1B-chinese:Q4_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lastmass/llama3.2-1B-chinese:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf lastmass/llama3.2-1B-chinese:Q4_K_MUse Docker
docker model run hf.co/lastmass/llama3.2-1B-chinese:Q4_K_MQuick Links
continued pretraining with wikipedia-zh
dataset = load_dataset("wikimedia/wikipedia", "20231101.zh", split = "train",)
and sft with FreedomIntelligence/alpaca-gpt4-zh
alpaca_dataset = load_dataset("FreedomIntelligence/alpaca-gpt4-zh", split = "train")
using unsloth to fine-tuning the model
use the model-unsloth.gguf file or model-unsloth-Q4_K_M.gguf file in llama.cpp or a UI based system like GPT4All.
aslo you can make the ollama model with modelfile
template
以下是描述一项任务的指令。请输出合适的内容回应指令。
### Input:
大象和猎豹的奔跑速度谁更快，简单说明原因.
### Response:
猎豹的奔跑速度比大象快得多。
猎豹：是世界上奔跑速度最快的陆地动物之一，短距离内可以达到约 100-120 公里/小时（约 60-75 英里/小时）。
大象：虽然大象体型巨大，但它们的速度较慢，奔跑速度最高约为 40 公里/小时（约 25 英里/小时）。
因此，猎豹在速度上远远超过了大象。
### Input:
鱼为什么能在水里呼吸。
### Response:
鱼能够在水中呼吸，主要是因为它们有一种特殊的呼吸器官——鳃。鳃能够从水中提取氧气，并排出二氧化碳，这个过程使鱼能够在水中生存。
{{ if .Prompt }}
### 指令：
{{ .Prompt }}
{{ end }}### 回应：
{{ .Response }}<|end_of_text|>
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Model tree for lastmass/llama3.2-1B-chinese
Base model
meta-llama/Llama-3.2-1B
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf lastmass/llama3.2-1B-chinese:Q4_K_M# Run inference directly in the terminal: llama-cli -hf lastmass/llama3.2-1B-chinese:Q4_K_M