Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use 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 tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build 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 tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
File size: 3,260 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | #pragma once
#include "server-common.h"
#include "server-task.h"
#include "sampling.h"
#include "speculative.h"
#include <climits>
#include <functional>
#include <limits>
#include <memory>
#include <string>
#include <vector>
namespace server_schema {
struct field_eval_context {
task_params & params;
const llama_vocab * vocab = nullptr;
const std::vector<llama_logit_bias> * logit_bias_eog = nullptr;
field_eval_context(task_params & params) : params(params) {}
};
using field_handler = std::function<void(field_eval_context &, const json &)>;
struct field {
std::vector<const char *> name;
const char * desc = "";
field_handler custom_handler;
field() = default;
field(const char * n) : name({n}) {}
virtual ~field() = default;
field * set_desc(const char * s) {
desc = s;
return this;
}
// if 'name' is present, use it, otherwise look for aliases following the order they were added
field * add_alias(const char * n) {
name.push_back(n);
return this;
}
field * set_handler(field_handler h) { this->custom_handler = h; return this; }
virtual void eval(field_eval_context & ctx, const json & data) = 0;
};
template <typename T = int32_t>
struct field_num : public field {
T & val;
T min = std::numeric_limits<T>::lowest();
T max = std::numeric_limits<T>::max();
bool is_hard_limit = false; // if true, throw error if the value is invalid
field_num(const char * n, T & val) : field(n), val(val) {}
// limits are inclusive, min <= value <= max
field_num * set_limits(T min, T max) {
this->min = min;
this->max = max;
return this;
}
field_num * set_hard_limits(T min, T max) {
set_limits(min, max);
is_hard_limit = true;
return this;
}
virtual void eval(field_eval_context & ctx, const json & data) override;
};
struct field_str : public field {
field_str(const char * n) : field(n) {}
virtual void eval(field_eval_context & ctx, const json & data) override;
};
struct field_bool : public field {
bool & val;
field_bool(const char * n, bool & val) : field(n), val(val) {}
virtual void eval(field_eval_context & ctx, const json & data) override;
};
struct field_json : public field {
field_json(const char * n) : field(n) {}
virtual void eval(field_eval_context & ctx, const json & data) override;
};
struct field_nested : public field {
std::vector<std::unique_ptr<field>> subfields;
field_nested(const char * n) : field(n) {}
field_nested * add_subfield(field * f) {
subfields.emplace_back(std::unique_ptr<field>(f));
return this;
}
virtual void eval(field_eval_context & ctx, const json & data) override;
};
std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(
const common_params & params_base,
task_params & params);
task_params eval_llama_cmpl_schema(
const llama_vocab * vocab,
const common_params & params_base,
const int n_ctx_slot,
const std::vector<llama_logit_bias> & logit_bias_eog,
const json & data);
} // namespace server_schema
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